text:
User talk:Malcolm Farmer

I hope the "grandma" question I asked 216 will work: "do you want to do this, or shall I do that?" --Ed Poor -- explain how the article count just jumped 5000 articles in a day user_talk:hfastedge

Grey Loris & Primates
I can not find a critter named Grey Loris in the taxonomic records. What is it called now? Also, you may want to join WikiProject Primates. - UtherSRG 15:26, 20 Feb 2004 (UTC)

Your uploaded image
Thanks for taking the photo Image:Scott memorial binton.jpg and uploading it. I notice it currently doesn't have an image copyright tag. Could you add one to let us know how you want to license it? (You can use if you release it under the GFDL, or  if you want to put it in the public domain, etc.) Thanks so much,    – Quadell (talk) (help)   02:31, Oct 4, 2004 (UTC)

 * Hi,

 * i have copyied the photos Image:Commodore_4032.jpg and Image:Commodore 4040.jpg to Wiki commons and noticed that doesn't have also an image copyright tag. --Fedi 18:36, 11 Nov 2004 (UTC)

--Fine. I've Marked them as PD. Malcolm Farmer 20:23, 24 Nov 2004 (UTC)

Flatulence: starch vs oligosaccharides
I've started a discussion on this subject at Talk:Flatulence. Since it looks like you wrote the text in question, I'd appreciate your input, although I realise it's nearly three years since you wrote that :) Ta. -- JTN 22:43, 2004 Oct 14 (UTC)

Adminship
May I nominate you for adminship? uc 22:02, 8 Nov 2004 (UTC)

I have nominated you for adminship at Requests for adminship/Malcolm Farmer. Please edit that page and indicate your acceptance, as some people involved in adminship matters now expect that to be done even though you have already agreed to be nominated. Please be aware that there are baseless objections to many adminship nominations; do not be upset if that should occur. If you have any questions or comments, you can contact me on my talk page or via email. The Uninvited Co., Inc. 16:15, 1 Dec 2004 (UTC)
 * Since you accepted the nomination, would you mind answering the candidate questions on Requests for adminship? They will aid users that don't know you in their decision. [[User:MacGyverMagic|Mgm|(talk)]] 21:15, Dec 1, 2004 (UTC)

Warning
Please do not break the Three revert rule. You appear to have five reverts in less than 15 minutes. There's really nothing to be gained by doing this, and in situations where one party is blocked for violating the rule, we must treat all violators equally. Please let others revert the article as needed. Cool Hand Luke  12:28, 4 Dec 2004 (UTC)

Congratulations, Malcolm!
Congratulations! It's my pleasure to let you know that, consensus being reached, you are now an administrator. You should read the relevant policies and other pages linked to from the administrators' reading list before carrying out tasks like deletion, protection, banning users, and editing protected pages such as the Main Page. Most of what you do is easily reversible by other sysops, apart from page history merges and image deletion, so please be especially careful with those. You might find the new administrators' how-to guide helpful. Cheers! -- Cecropia | explains it all ® 16:58, 8 Dec 2004 (UTC)

Article Licensing
Hi, I've started a drive to get users to multi-license all of their contributions that they've made to either (1) all U.S. state, county, and city articles or (2) all articles, using the Creative Commons Attribution-Share Alike (CC-by-sa) v1.0 and v2.0 Licenses or into the public domain if they prefer. The CC-by-sa license is a true free documentation license that is similar to Wikipedia's license, the GFDL, but it allows other projects, such as WikiTravel, to use our articles. Since you are among the top 1000 Wikipedians by edits, I was wondering if you would be willing to multi-license all of your contributions or at minimum those on the geographic articles. Over 90% of people asked have agreed. For More Information:
 * Multi-Licensing FAQ - Lots of questions answered
 * Multi-Licensing Guide
 * Free the Rambot Articles Project

To allow us to track those users who muli-license their contributions, many users copy and paste the " " template into their user page, but there are other options at Template messages/User namespace. The following examples could also copied and pasted into your user page:

 * Option 1
 * I agree to multi-license all my contributions, with the exception of my user pages, as described below:

OR
 * Option 2
 * I agree to multi-license all my contributions to any U.S. state, county, or city article as described below:

Or if you wanted to place your work into the public domain, you could replace " " with "  ". If you only prefer using the GFDL, I would like to know that too. Please let me know what you think at my talk page. It's important to know either way so no one keeps asking. -- Ram-Man (comment| talk)

Unverified images
Hi! Thanks for uploading the following image:

 * Image:Isaac_disraeli.jpg

I notice it currently doesn't have an image copyright tag. Could you add one to let us know its copyright status? (You can use if you release it under the GNU Free Documentation License,  if you claim fair use, etc.) If you don't know what any of this means, just let me know at my talk page where you got the images and I'll tag them for you. Thanks so much. [[User:Poccil|Peter O. (Talk, automation script)]] 22:20, Dec 10, 2004 (UTC)

P.S. You can help tag other images at User:Yann/Untagged_Images. Thanks again.

Thanks
Hi, thanks for reverting the David Blunkett vandal -- you reverted him just before I got round to it again, while I was busy barring him for 48 hours first! The idiot was daft enough to actually seriously libel Blunkett in his first round of edits an hour ago, so I left a strong warning on his page then, so no mercy this time! -- Arwel 13:57, 11 Feb 2005 (UTC)

Napoleon Gambit on vfd
Hi Malcolm,

I see that you have made some edits to the new Napoleon Gambit article. I have now listed it for deletion because the variations given seemed to be all wrong, and becuase "Napoleon Gambit" is not a chess opening. Also the article looked like an unchessic mess (None of this is your fault). If you are interested the debate is at Votes for deletion/Napoleon Gambit. Sjakkalle 09:48, 23 Mar 2005 (UTC)

"Old Encyclopedia"
I notice that at least some of the very old "from an 1880s edition of an unnamed encylopedia" stuff seems to have been put there by you (see Talk:Pope Martin V). The rationale you provide for not naming the encyclopedia is, in fact, not valid, and not how we do things at this point. Am I correct in assuming that all of these references are to the 9th edition of the Encyclopaedia Britannica? john k 16:04, 15 Apr 2005 (UTC)

Thanks
Thanks for helping me revert that spam-vandal. For some reason, my computer (or Wikipedia?) was being really slow and it was taking forever for me to rollback his edits. — Knowledge Seeker দ 09:09, 19 Apr 2005 (UTC)

My Talk page
Hello! I wondered why you'd deleted a couple of (innocuous) messages from my Talk page?

Punch cartoon commentary
If I'm not missing something, your interpretation varies from mine only in that it identifies the swim as a cross-channel swim, and Smith as coach rather than merely rower. We both, I think, identify and agree on the people, the nearness of prorogation and the pursuing issues/waves of free education and land purchase. I'm happy if you want to edit the caption, especially to add info on the cross-channel business. Any other diffs are possibly to do with me trying to be concise. Enjoy. And yes, I might edit your edits until we reach a consensus :) --Tagishsimon (talk)

 * Captain Matthew Webb was the first man to swim the English channel (between England and France), in 1875. He used breaststroke, swimming 21.26 miles in 21 hours and 45 minutes. No other man or woman swam the channel for the next 31 years. From, FWIW. --Tagishsimon (talk)

Your mom
Hello

You deleted this article as a speedy (I guess) due to vandalism. You forgot to check its rather large past history, which contained a full article, which has been referenced by media sources, etc. Any chance of a fix-up job here? --Kiand 19:26, 14 Jun 2005 (UTC)

 * I restored the article. Thanks, Kiand. Malcolm, don't forget to check the history of an article before deleting it. — Knowledge Seeker দ 17:31, 15 Jun 2005 (UTC)

220.239.43.192
220.239.43.192 is really starting to get on my nerves... Gringo300 04:05, 8 October 2005 (UTC)

You should try looking at the bands bios on multiple sites... I.E. Purevolume, their original website (www.SDAHM.com) is no longer up

Inoculation
Of which you are the earliest recorded author, looks to me as if it could be a Good Article now. Midgley 13:54, 13 March 2006 (UTC)

:)
Thanks for the revert! --F a ng Aili 說嗎? 20:38, 3 April 2006 (UTC)

*whistle*
Good god. You've been on WP for over five years. That's bloody impressive, that is! Well done! DS 14:23, 4 July 2006 (UTC)

CBM 8032 or CBM 4032 ?
Because of the size of the screen I would say that is a CBM 8032 rather than a 4032, isn´t it?

You discribe it as a 4032. ;-) --84.145.193.224 20:07, 28 August 2006 (UTC)

I just went upstairs and looked. 4032 is what it says on the front panel. Malcolm Farmer 23:16, 28 August 2006 (UTC)

 * Hi, I found out, that there are two different editions of the 4032: [thin (4032-a)] and [fat (4032-b)]. Your picture shows the fat one ;-) --84.145.171.54 16:45, 2 September 2006 (UTC)

Nice work on the spam one
Nice collection - time for a "spam im" warning? I put a basic one on. Cheers --Nigel (Talk) 19:15, 31 August 2006 (UTC)

 * OK I got another one so did it anyway - cheers - --Nigel (Talk) 19:18, 31 August 2006 (UTC)

Mary Enda Tookey
Why did you delete the entry on Mary Enda Tookey?

OnShare
FYI there is a WP:CSD for copyvio's. The tag can take weeks or even months for something to get deleted as long as the page has been created in the last 48 hours you can use the CSD. Whisp e ring 20:12, 17 October 2006 (UTC)

Theory
As any other theory it's just a theory, not a fact. They are some evidence that can be interpreted in a different way, Big Bang's become a religion of some sort, there are lesser evidence day by day, and why every such theory will always be just a theory is did anyone been there and experenced it?

Move
Could you move my page User:Lazylaces/Main to User:Lazylaces? and the talk pages. I can't gat the orange bar of death anymore. Cheers, Lazylaces (Talk to me 14:02, 2 May 2007 (UTC)

Typo redirect Geoffrey thorne
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Speedy deletion of Loch ness monster
A tag has been placed on Loch ness monster, requesting that it be speedily deleted from Wikipedia. This has been done under section G1 of the criteria for speedy deletion, because the page appears to have no meaningful content or history, and the text is unsalvageably incoherent. If the page you created was a test, please use the sandbox for any other experiments you would like to do. Feel free to leave a message on my talk page if you have any questions about this.

If you think that this notice was placed here in error, you may contest the deletion by adding  to the top of the page (just below the existing speedy deletion or "db" tag), coupled with adding a note on the talk page explaining your position, but be aware that once tagged for speedy deletion, if the article meets the criterion it may be deleted without delay. Please do not remove the speedy deletion tag yourself, but don't hesitate to add information to the article that would would render it more in conformance with Wikipedia's policies and guidelines. Lastly, please note that if the article does get deleted, you can contact one of these admins to request that a copy be emailed to you. ṜέđṃάяķvίʘĨїήīṣŢ Drop me a line 20:03, 11 April 2008 (UTC)

Image copyright problem with Image:Young marvelman annual.png
Thanks for uploading Image:Young marvelman annual.png. You've indicated that the image is being used under a claim of fair use, but you have not provided an adequate explanation for why it meets Wikipedia's requirements for such images. In particular, for each page the image is used on, the image must have an explanation linking to that page which explains why it needs to be used on that page. Can you please check

 * That there is a non-free use rationale on the image's description page for each article the image is used in.
 * That every article it is used on is linked to from its description page.

This is an automated notice by FairuseBot. For assistance on the image use policy, see Media copyright questions. --FairuseBot (talk) 12:23, 5 November 2008 (UTC)

Some old pages
Hi Malcolm, the "new topics" page is still around at New topics, and I've just replaced the list of articles there with an explanation of how the page was used. Could you take a peek at that page? Feel free to correct any errorrs I made there and/or add your own notes. I've also history merged your user page. Hope you don't mind. Graham 87 12:45, 21 January 2009 (UTC)

Image:Richard jefferies.jpg missing description details
Dear uploader: The media file you uploaded as Image:Richard jefferies.jpg is missing a description and/or other details on its image description page. If possible, please add this information. This will help other editors to make better use of the image, and it will be more informative for readers.

If you have any questions please see Help:Image page. Thank you. Sfan00 IMG (talk) 15:02, 10 February 2009 (UTC)

Worcester
Hi ! an article you have contributed to, has been selected for the Wikipedia Version 0.8. offline release on DVD and iPhone. If you would like to make any last minutes changes or improvements, you are most welcome to do so. Deadline is midnight UTC on Monday, 11 October. See also: Wikipedia talk:WikiProject Worcestershire/Archive 1.--Kudpung (talk) 04:52, 27 September 2010 (UTC)

Speedy deletion nomination of Samuel Butler (poet)

A tag has been placed on Samuel Butler (poet) requesting that it be speedily deleted from Wikipedia. This has been done under section G12 of the criteria for speedy deletion, because the article appears to be a clear copyright infringement. For legal reasons, we cannot accept copyrighted text or images borrowed from other web sites or printed material, and as a consequence, your addition will most likely be deleted. You may use external websites as a source of information, but not as a source of sentences. This part is crucial: say it in your own words.

If the external website belongs to you, and you want to allow Wikipedia to use the text — which means allowing other people to modify it — then you must verify that externally by one of the processes explained at Donating copyrighted materials. If you are not the owner of the external website but have permission from that owner, see Requesting copyright permission. You might want to look at Wikipedia's policies and guidelines for more details, or ask a question here.

If you think that this notice was placed here in error, you may contest the deletion by adding to the top of the page that has been nominated for deletion (just below the existing speedy deletion, or "db", tag; if no such tag exists, then the page is no longer a speedy delete candidate and adding a hang-on tag is unnecessary), coupled with adding a note on the talk page explaining your position, but be aware that once tagged for speedy deletion, if the page meets the criterion, it may be deleted without delay. Please do not remove the speedy deletion tag yourself, but don't hesitate to add information to the page that would render it more in conformance with Wikipedia's policies and guidelines. Phearson (talk) 00:07, 8 March 2011 (UTC)

 * I've removed the tag. Graham 87 02:09, 8 March 2011 (UTC)

Notification of pending suspension of administrative permissions due to inactivity
Following a community discussion in June 2011, consensus was reached to provisionally suspend the administrative permissions of users who have been inactive for one year (i.e. administrators who have not made any edits or logged actions in over one year). As a result of this discussion, your administrative permissions will be removed pending your return if you do not return to activity within the next month. If you wish to have these permissions reinstated should this occur, please post to the Bureaucrats' noticeboard and the userright will be restored per the re-sysopping process (i.e., as long as the attending bureaucrats are reasonably satisfied that your account has not been compromised and that your inactivity did not have the effect of evading scrutiny of any actions which might have led to sanctions). This removal of access is procedural only, and not intended to reflect negatively upon you in any way. We wish you the best in future endeavors, and thank you for your past administrative efforts. MadmanBot (talk) 01:06, 1 March 2012 (UTC)

Notification of imminent suspension of administrative permissions due to inactivity
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Suspension of administrative permissions due to inactivity
Following a community discussion in June 2011, consensus was reached to provisionally suspend the administrative permissions of users who have been inactive for one year (i.e. administrators who have not made any edits or logged actions in over one year). As a result of this discussion, your administrative permissions have been removed pending your return. If you wish to have these permissions reinstated, please post to the Bureaucrats' noticeboard and the userright will be restored per the re-sysopping process (i.e. as long as the attending bureaucrats are reasonably satisfied that your account has not been compromised and that your inactivity did not have the effect of evading scrutiny of any actions which might have led to sanctions). This removal of access is procedural only, and not intended to reflect negatively upon you in any way. We wish you the best in future endeavors, and thank you for your past administrative efforts. WJBscribe (talk) 16:06, 1 April 2012 (UTC)

HomePage listed at Redirects for discussion
An editor has asked for a discussion to address the redirect HomePage. Since you had some involvement with the HomePage redirect, you might want to participate in the redirect discussion (if you have not already done so). ''TheChampionMan1234 00:41, 20 October 2012 (UTC)

Notice of change
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DefinitionOfLogic listed at Redirects for discussion
An editor has asked for a discussion to address the redirect DefinitionOfLogic. Since you had some involvement with the DefinitionOfLogic redirect, you might want to participate in the redirect discussion if you have not already done so. Steel1943 (talk) 01:54, 15 August 2015 (UTC)

ArbCom elections are now open!
MediaWiki message delivery (talk) 08:51, 23 November 2015 (UTC)

Proposed deletion of Phantom cat

The article Phantom cat has been proposed for deletion  because of the following concern:
 * Unencyclopedic list of speculation about non-existent animals.

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Just to let you know
You have been mentioned at Missing Wikipedians. Ottawahitech (talk) 16:08, 8 September 2016 (UTC)please ping me

Historical anniversaries/September 21 listed at Redirects for discussion
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Historical anniversaries/September 2 listed at Redirects for discussion
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Historical anniversaries/July 20 listed at Redirects for discussion
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Speedy deletion nomination of HomePage

Hello, and welcome to Wikipedia. This is a notice to inform you that a tag has been placed on HomePage requesting that it be speedily deleted from Wikipedia. This has been done under section A3 of the criteria for speedy deletion, because it is an article with no content whatsoever, or whose contents consist only of external links, a "See also" section, book references, category tags, template tags, interwiki links, images, a rephrasing of the title, a question that should have been asked at the help or reference desks, or an attempt to contact the subject of the article. Please see Wikipedia:Stub for our minimum information standards for short articles. Also please note that articles must be on notable subjects and should provide references to reliable sources that verify their content.

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Proposed deletion of 182

The article 182 has been proposed for deletion because of the following concern: "article is not about 182"

While all constructive contributions to Wikipedia are appreciated, pages may be deleted for any of several reasons.

You may prevent the proposed deletion by removing the notice, but please explain why in your edit summary or on the article's talk page.

Please consider improving the page to address the issues raised. Removing will stop the proposed deletion process, but other deletion processes exist. In particular, the speedy deletion process can result in deletion without discussion, and articles for deletion allows discussion to reach consensus for deletion. Jax 0677 (talk) 07:05, 16 November 2018 (UTC)
 * I've reverted the recent vandalism to the page, and it's back to normal now. Graham 87 16:07, 16 November 2018 (UTC)

Invitation to join the Fifteen Year Society
Dear ,

I'd like to extend a cordial invitation to you to join the Fifteen Year Society, an informal group for editors who've been participating in the Wikipedia project for fifteen years or more.

Best regards, Urhixidur (talk) 16:36, 6 June 2019 (UTC)

Proposed deletion of Stormbringer

The article Stormbringer has been proposed for deletion because of the following concern: "No evidence this fictional object passes GNG/NFICTION."

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Please consider improving the page to address the issues raised. Removing will stop the proposed deletion process, but other deletion processes exist. In particular, the speedy deletion process can result in deletion without discussion, and articles for deletion allows discussion to reach consensus for deletion. Piotr Konieczny aka Prokonsul Piotrus| reply here 12:25, 21 January 2020 (UTC)

Proposed deletion of Yahoo (Gulliver's Travels)

The article Yahoo (Gulliver's Travels) has been proposed for deletion because of the following concern: "The coverage (references, external links, etc.) does not seem sufficient to justify this article passing General notability guideline and the more detailed Notability (fiction) requirement. WP:BEFORE did not reveal any significant coverage on Gnews, Gbooks or Gscholar. If you disagree and deprod this, please explain how it meets them on the talk page here in the form of 'This article meets criteria A and B because...' and ping me back through WP:ECHO or by leaving a note at User talk:Piotrus. Thank you."

While all constructive contributions to Wikipedia are appreciated, pages may be deleted for any of several reasons.

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Please consider improving the page to address the issues raised. Removing will stop the proposed deletion process, but other deletion processes exist. In particular, the speedy deletion process can result in deletion without discussion, and articles for deletion allows discussion to reach consensus for deletion. Piotr Konieczny aka Prokonsul Piotrus| reply here 09:28, 21 January 2021 (UTC)

A kitten for you!
You made cat page! Here's a cat!

KrystopherNystrom (talk) 05:39, 23 August 2021 (UTC)
 * Nope, he didn't, he just made the first surviving edit to that page. Many early edits are lost; see Usemod article histories. Graham 87 16:58, 23 August 2021 (UTC)

Nomination of Stormbringer for deletion
A discussion is taking place as to whether the article Stormbringer is suitable for inclusion in Wikipedia according to Wikipedia's policies and guidelines or whether it should be deleted.

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title: User talk:Malcolm Farmer


text:
Corruption (disambiguation)

Corruption is the abuse of power for personal gain.

Corruption may also refer to:

Entertainment

 * Corruption (1933 film), an American crime film
 * Corruption (1963 film), an Italian drama film
 * Corruption (1968 film), a British horror film
 * Corruption (1988 video game), a 1988 adventure game
 * Metroid Prime 3: Corruption, a 2007 action-adventure game
 * Corruption (play), a play by J. T. Rogers
 * "Corruption", a song by Northlane from Discoveries, 2011
 * "Corruption" (Star Wars: The Clone Wars)

Other

 * Corruption (linguistics), the idea that language change constitutes a degradation in quality
 * Data corruption, errors in computer data
 * A copying error in textual criticism

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Corruption (disambiguation)


text:
Hidden Markov model

A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or "hidden") Markov process (referred to as $$ X $$). An HMM requires that there be an observable process $$ Y $$ whose outcomes depend on the outcomes of $$X$$ in a known way. Since $$ X $$ cannot be observed directly, the goal is to learn about state of $$X$$ by observing $$ Y. $$ By definition of being a Markov model, an HMM has an additional requirement that the outcome of $$ Y $$ at time $$ t = t_0 $$ must be "influenced" exclusively by the outcome of $$ X $$ at $$ t = t_0 $$ and that the outcomes of $$ X $$ and $$ Y $$ at $$ t < t_0 $$ must be conditionally independent of $$ Y $$ at $$ t=t_0 $$ given $$ X $$ at time $$ t = t_0. $$ Estimation of the parameters in an HMM can be performed using maximum likelihood. For linear chain HMMs, the Baum–Welch algorithm can be used to estimate the parameters.

Hidden Markov models are known for their applications to thermodynamics, statistical mechanics, physics, chemistry, economics, finance, signal processing, information theory, pattern recognition—such as speech, handwriting, gesture recognition, part-of-speech tagging, musical score following, partial discharges and bioinformatics.

Definition
Let $$X_n$$ and $$Y_n$$ be discrete-time stochastic processes and $$n\geq 1$$. The pair $$(X_n,Y_n)$$ is a hidden Markov model if

 * $$X_n$$ is a Markov process whose behavior is not directly observable ("hidden");
 * $$\operatorname{\mathbf{P}}\bigl(Y_n \in A\ \bigl|\ X_1=x_1,\ldots,X_n=x_n\bigr)=\operatorname{\mathbf{P}}\bigl(Y_n \in A\ \bigl|\ X_n=x_n\bigr),$$

 * for every $$n\geq 1,$$ $$x_1,\ldots, x_n,$$ and every Borel set $$A$$.

Let $$X_t$$ and $$Y_t$$ be continuous-time stochastic processes. The pair $$(X_t,Y_t)$$ is a hidden Markov model if
 * $$X_t$$ is a Markov process whose behavior is not directly observable ("hidden");
 * $$\operatorname{\mathbf{P}}(Y_{t_0} \in A \mid \{X_t \in B_t\}_{ t\leq t_0}) = \operatorname{\mathbf{P}}(Y_{t_0} \in A \mid X_{t_0} \in B_{t_0})$$,

 * for every $$ t_0, $$ every Borel set $$ A, $$ and every family of Borel sets $$ \{B_t\}_{t \leq t_0}. $$

Terminology
The states of the process $$X_n$$ (resp. $$X_t)$$ are called hidden states, and $$\operatorname{\mathbf{P}}\bigl(Y_n \in A \mid X_n=x_n\bigr)$$ (resp. $$\operatorname{\mathbf{P}}\bigl(Y_t \in A \mid X_t \in B_t\bigr))$$ is called emission probability or output probability.

Drawing balls from hidden urns
[[File:HiddenMarkovModel.svg|right|thumb|300px| Figure 1. Probabilistic parameters of a hidden Markov model (example)

X — states

y — possible observations

a — state transition probabilities

b — output probabilities]] In its discrete form, a hidden Markov process can be visualized as a generalization of the urn problem with replacement (where each item from the urn is returned to the original urn before the next step). Consider this example: in a room that is not visible to an observer there is a genie. The room contains urns X1, X2, X3, ... each of which contains a known mix of balls, with each ball having a unique label y1, y2, y3, ... . The genie chooses an urn in that room and randomly draws a ball from that urn. It then puts the ball onto a conveyor belt, where the observer can observe the sequence of the balls but not the sequence of urns from which they were drawn. The genie has some procedure to choose urns; the choice of the urn for the n-th ball depends only upon a random number and the choice of the urn for the (n − 1)-th ball. The choice of urn does not directly depend on the urns chosen before this single previous urn; therefore, this is called a Markov process. It can be described by the upper part of Figure 1.

The Markov process itself cannot be observed, only the sequence of labeled balls, thus this arrangement is called a "hidden Markov process". This is illustrated by the lower part of the diagram shown in Figure 1, where one can see that balls y1, y2, y3, y4 can be drawn at each state. Even if the observer knows the composition of the urns and has just observed a sequence of three balls, e.g. y1, y2 and y3 on the conveyor belt, the observer still cannot be sure which urn (i.e., at which state) the genie has drawn the third ball from. However, the observer can work out other information, such as the likelihood that the third ball came from each of the urns.

Weather guessing game
Consider two friends, Alice and Bob, who live far apart from each other and who talk together daily over the telephone about what they did that day. Bob is only interested in three activities: walking in the park, shopping, and cleaning his apartment. The choice of what to do is determined exclusively by the weather on a given day. Alice has no definite information about the weather, but she knows general trends. Based on what Bob tells her he did each day, Alice tries to guess what the weather must have been like.

Alice believes that the weather operates as a discrete Markov chain. There are two states, "Rainy" and "Sunny", but she cannot observe them directly, that is, they are hidden from her. On each day, there is a certain chance that Bob will perform one of the following activities, depending on the weather: "walk", "shop", or "clean". Since Bob tells Alice about his activities, those are the observations. The entire system is that of a hidden Markov model (HMM).

Alice knows the general weather trends in the area, and what Bob likes to do on average. In other words, the parameters of the HMM are known. They can be represented as follows in Python: In this piece of code,  represents Alice's belief about which state the HMM is in when Bob first calls her (all she knows is that it tends to be rainy on average). The particular probability distribution used here is not the equilibrium one, which is (given the transition probabilities) approximately. The  represents the change of the weather in the underlying Markov chain. In this example, there is only a 30% chance that tomorrow will be sunny if today is rainy. The  represents how likely Bob is to perform a certain activity on each day. If it is rainy, there is a 50% chance that he is cleaning his apartment; if it is sunny, there is a 60% chance that he is outside for a walk.

A similar example is further elaborated in the Viterbi algorithm page.

Structural architecture
The diagram below shows the general architecture of an instantiated HMM. Each oval shape represents a random variable that can adopt any of a number of values. The random variable x(t) is the hidden state at time $t$ (with the model from the above diagram, x(t) ∈ { x1, x2, x3 }). The random variable y(t) is the observation at time $t$ (with y(t) ∈ { y1, y2, y3, y4 }). The arrows in the diagram (often called a trellis diagram) denote conditional dependencies.

From the diagram, it is clear that the conditional probability distribution of the hidden variable x(t) at time $t$, given the values of the hidden variable $x$ at all times, depends only on the value of the hidden variable x(t − 1); the values at time t − 2 and before have no influence. This is called the Markov property. Similarly, the value of the observed variable y(t) only depends on the value of the hidden variable x(t) (both at time $t$).

In the standard type of hidden Markov model considered here, the state space of the hidden variables is discrete, while the observations themselves can either be discrete (typically generated from a categorical distribution) or continuous (typically from a Gaussian distribution). The parameters of a hidden Markov model are of two types, transition probabilities and emission probabilities (also known as output probabilities). The transition probabilities control the way the hidden state at time $t$ is chosen given the hidden state at time $$t-1$$.

The hidden state space is assumed to consist of one of $N$ possible values, modelled as a categorical distribution. (See the section below on extensions for other possibilities.) This means that for each of the $N$ possible states that a hidden variable at time $t$ can be in, there is a transition probability from this state to each of the $N$ possible states of the hidden variable at time $$t+1$$, for a total of $$N^2$$ transition probabilities. Note that the set of transition probabilities for transitions from any given state must sum to 1. Thus, the $$N \times N$$ matrix of transition probabilities is a Markov matrix. Because any transition probability can be determined once the others are known, there are a total of $$N(N-1)$$ transition parameters.

In addition, for each of the $N$ possible states, there is a set of emission probabilities governing the distribution of the observed variable at a particular time given the state of the hidden variable at that time. The size of this set depends on the nature of the observed variable. For example, if the observed variable is discrete with $M$ possible values, governed by a categorical distribution, there will be $$M-1$$ separate parameters, for a total of $$N(M-1)$$ emission parameters over all hidden states. On the other hand, if the observed variable is an $M$-dimensional vector distributed according to an arbitrary multivariate Gaussian distribution, there will be $M$ parameters controlling the means and $$\frac {M(M+1)} 2$$ parameters controlling the covariance matrix, for a total of $$N \left(M + \frac{M(M+1)}{2}\right) = \frac {NM(M+3)} 2 = O(NM^2)$$ emission parameters. (In such a case, unless the value of $M$ is small, it may be more practical to restrict the nature of the covariances between individual elements of the observation vector, e.g. by assuming that the elements are independent of each other, or less restrictively, are independent of all but a fixed number of adjacent elements.)

Inference
[[File:HMMsequence.svg|thumb|400px|The state transition and output probabilities of an HMM are indicated by the line opacity in the upper part of the diagram. Given that we have observed the output sequence in the lower part of the diagram, we may be interested in the most likely sequence of states that could have produced it. Based on the arrows that are present in the diagram, the following state sequences are candidates:

5 3 2 5 3 2

4 3 2 5 3 2

3 1 2 5 3 2

We can find the most likely sequence by evaluating the joint probability of both the state sequence and the observations for each case (simply by multiplying the probability values, which here correspond to the opacities of the arrows involved). In general, this type of problem (i.e. finding the most likely explanation for an observation sequence) can be solved efficiently using the Viterbi algorithm.]]

Several inference problems are associated with hidden Markov models, as outlined below.

Probability of an observed sequence
The task is to compute in a best way, given the parameters of the model, the probability of a particular output sequence. This requires summation over all possible state sequences:

The probability of observing a sequence
 * $$Y=y(0), y(1),\dots,y(L-1)\,$$

of length L is given by
 * $$P(Y)=\sum_{X}P(Y\mid X)P(X),\,$$

where the sum runs over all possible hidden-node sequences
 * $$X=x(0), x(1), \dots, x(L-1).\,$$

Applying the principle of dynamic programming, this problem, too, can be handled efficiently using the forward algorithm.

Probability of the latent variables
A number of related tasks ask about the probability of one or more of the latent variables, given the model's parameters and a sequence of observations $$y(1),\dots,y(t).$$

Filtering
The task is to compute, given the model's parameters and a sequence of observations, the distribution over hidden states of the last latent variable at the end of the sequence, i.e. to compute $$P(x(t)\ |\ y(1),\dots,y(t))$$. This task is used when the sequence of latent variables is thought of as the underlying states that a process moves through at a sequence of points in time, with corresponding observations at each point. Then, it is natural to ask about the state of the process at the end.

This problem can be handled efficiently using the forward algorithm. An example is when the algorithm is applied to a Hidden Markov Network to determine $$\mathrm{P}\big( h_t \ | v_{1:t} \big)$$.

Smoothing
This is similar to filtering but asks about the distribution of a latent variable somewhere in the middle of a sequence, i.e. to compute $$P(x(k)\ |\ y(1), \dots, y(t))$$ for some $$k < t$$. From the perspective described above, this can be thought of as the probability distribution over hidden states for a point in time k in the past, relative to time t.

The forward-backward algorithm is a good method for computing the smoothed values for all hidden state variables.

Most likely explanation
The task, unlike the previous two, asks about the joint probability of the entire sequence of hidden states that generated a particular sequence of observations (see illustration on the right). This task is generally applicable when HMM's are applied to different sorts of problems from those for which the tasks of filtering and smoothing are applicable. An example is part-of-speech tagging, where the hidden states represent the underlying parts of speech corresponding to an observed sequence of words. In this case, what is of interest is the entire sequence of parts of speech, rather than simply the part of speech for a single word, as filtering or smoothing would compute.

This task requires finding a maximum over all possible state sequences, and can be solved efficiently by the Viterbi algorithm.

Statistical significance
For some of the above problems, it may also be interesting to ask about statistical significance. What is the probability that a sequence drawn from some null distribution will have an HMM probability (in the case of the forward algorithm) or a maximum state sequence probability (in the case of the Viterbi algorithm) at least as large as that of a particular output sequence? When an HMM is used to evaluate the relevance of a hypothesis for a particular output sequence, the statistical significance indicates the false positive rate associated with failing to reject the hypothesis for the output sequence.

Learning
The parameter learning task in HMMs is to find, given an output sequence or a set of such sequences, the best set of state transition and emission probabilities. The task is usually to derive the maximum likelihood estimate of the parameters of the HMM given the set of output sequences. No tractable algorithm is known for solving this problem exactly, but a local maximum likelihood can be derived efficiently using the Baum–Welch algorithm or the Baldi–Chauvin algorithm. The Baum–Welch algorithm is a special case of the expectation-maximization algorithm.

If the HMMs are used for time series prediction, more sophisticated Bayesian inference methods, like Markov chain Monte Carlo (MCMC) sampling are proven to be favorable over finding a single maximum likelihood model both in terms of accuracy and stability. Since MCMC imposes significant computational burden, in cases where computational scalability is also of interest, one may alternatively resort to variational approximations to Bayesian inference, e.g. Indeed, approximate variational inference offers computational efficiency comparable to expectation-maximization, while yielding an accuracy profile only slightly inferior to exact MCMC-type Bayesian inference.

Applications

HMMs can be applied in many fields where the goal is to recover a data sequence that is not immediately observable (but other data that depend on the sequence are). Applications include:
 * Computational finance
 * Single-molecule kinetic analysis
 * Neuroscience
 * Cryptanalysis
 * Speech recognition, including Siri
 * Speech synthesis
 * Part-of-speech tagging
 * Document separation in scanning solutions
 * Machine translation
 * Partial discharge
 * Gene prediction
 * Handwriting recognition
 * Alignment of bio-sequences
 * Time series analysis
 * Activity recognition
 * Protein folding
 * Sequence classification
 * Metamorphic virus detection
 * Sequence motif discovery (DNA and proteins)
 * DNA hybridization kinetics
 * Chromatin state discovery
 * Transportation forecasting
 * Solar irradiance variability

History
Hidden Markov models were described in a series of statistical papers by Leonard E. Baum and other authors in the second half of the 1960s. One of the first applications of HMMs was speech recognition, starting in the mid-1970s.

In the second half of the 1980s, HMMs began to be applied to the analysis of biological sequences, in particular DNA. Since then, they have become ubiquitous in the field of bioinformatics.

General state spaces
In the hidden Markov models considered above, the state space of the hidden variables is discrete, while the observations themselves can either be discrete (typically generated from a categorical distribution) or continuous (typically from a Gaussian distribution). Hidden Markov models can also be generalized to allow continuous state spaces. Examples of such models are those where the Markov process over hidden variables is a linear dynamical system, with a linear relationship among related variables and where all hidden and observed variables follow a Gaussian distribution. In simple cases, such as the linear dynamical system just mentioned, exact inference is tractable (in this case, using the Kalman filter); however, in general, exact inference in HMMs with continuous latent variables is infeasible, and approximate methods must be used, such as the extended Kalman filter or the particle filter.

Nowadays, inference in hidden Markov models is performed in nonparametric settings, where the dependency structure enables identifiability of the model and the learnability limits are still under exploration.

Bayesian modeling of the transitions probabilities
Hidden Markov models are generative models, in which the joint distribution of observations and hidden states, or equivalently both the prior distribution of hidden states (the transition probabilities) and conditional distribution of observations given states (the emission probabilities), is modeled. The above algorithms implicitly assume a uniform prior distribution over the transition probabilities. However, it is also possible to create hidden Markov models with other types of prior distributions. An obvious candidate, given the categorical distribution of the transition probabilities, is the Dirichlet distribution, which is the conjugate prior distribution of the categorical distribution. Typically, a symmetric Dirichlet distribution is chosen, reflecting ignorance about which states are inherently more likely than others. The single parameter of this distribution (termed the concentration parameter) controls the relative density or sparseness of the resulting transition matrix. A choice of 1 yields a uniform distribution. Values greater than 1 produce a dense matrix, in which the transition probabilities between pairs of states are likely to be nearly equal. Values less than 1 result in a sparse matrix in which, for each given source state, only a small number of destination states have non-negligible transition probabilities. It is also possible to use a two-level prior Dirichlet distribution, in which one Dirichlet distribution (the upper distribution) governs the parameters of another Dirichlet distribution (the lower distribution), which in turn governs the transition probabilities. The upper distribution governs the overall distribution of states, determining how likely each state is to occur; its concentration parameter determines the density or sparseness of states. Such a two-level prior distribution, where both concentration parameters are set to produce sparse distributions, might be useful for example in unsupervised part-of-speech tagging, where some parts of speech occur much more commonly than others; learning algorithms that assume a uniform prior distribution generally perform poorly on this task. The parameters of models of this sort, with non-uniform prior distributions, can be learned using Gibbs sampling or extended versions of the expectation-maximization algorithm.

An extension of the previously described hidden Markov models with Dirichlet priors uses a Dirichlet process in place of a Dirichlet distribution. This type of model allows for an unknown and potentially infinite number of states. It is common to use a two-level Dirichlet process, similar to the previously described model with two levels of Dirichlet distributions. Such a model is called a hierarchical Dirichlet process hidden Markov model, or HDP-HMM for short. It was originally described under the name "Infinite Hidden Markov Model" and was further formalized in "Hierarchical Dirichlet Processes".

Discriminative approach
A different type of extension uses a discriminative model in place of the generative model of standard HMMs. This type of model directly models the conditional distribution of the hidden states given the observations, rather than modeling the joint distribution. An example of this model is the so-called maximum entropy Markov model (MEMM), which models the conditional distribution of the states using logistic regression (also known as a "maximum entropy model"). The advantage of this type of model is that arbitrary features (i.e. functions) of the observations can be modeled, allowing domain-specific knowledge of the problem at hand to be injected into the model. Models of this sort are not limited to modeling direct dependencies between a hidden state and its associated observation; rather, features of nearby observations, of combinations of the associated observation and nearby observations, or in fact of arbitrary observations at any distance from a given hidden state can be included in the process used to determine the value of a hidden state. Furthermore, there is no need for these features to be statistically independent of each other, as would be the case if such features were used in a generative model. Finally, arbitrary features over pairs of adjacent hidden states can be used rather than simple transition probabilities. The disadvantages of such models are: (1) The types of prior distributions that can be placed on hidden states are severely limited; (2) It is not possible to predict the probability of seeing an arbitrary observation. This second limitation is often not an issue in practice, since many common usages of HMM's do not require such predictive probabilities.

A variant of the previously described discriminative model is the linear-chain conditional random field. This uses an undirected graphical model (aka Markov random field) rather than the directed graphical models of MEMM's and similar models. The advantage of this type of model is that it does not suffer from the so-called label bias problem of MEMM's, and thus may make more accurate predictions. The disadvantage is that training can be slower than for MEMM's.

Other extensions
Yet another variant is the factorial hidden Markov model, which allows for a single observation to be conditioned on the corresponding hidden variables of a set of $$K$$ independent Markov chains, rather than a single Markov chain. It is equivalent to a single HMM, with $$N^K$$ states (assuming there are $$N$$ states for each chain), and therefore, learning in such a model is difficult: for a sequence of length $$T$$, a straightforward Viterbi algorithm has complexity $$O(N^{2K} \, T)$$. To find an exact solution, a junction tree algorithm could be used, but it results in an $$O(N^{K+1} \, K \, T)$$ complexity. In practice, approximate techniques, such as variational approaches, could be used.

All of the above models can be extended to allow for more distant dependencies among hidden states, e.g. allowing for a given state to be dependent on the previous two or three states rather than a single previous state; i.e. the transition probabilities are extended to encompass sets of three or four adjacent states (or in general $$K$$ adjacent states). The disadvantage of such models is that dynamic-programming algorithms for training them have an $$O(N^K \, T)$$ running time, for $$K$$ adjacent states and $$T$$ total observations (i.e. a length-$$T$$ Markov chain). This extension has been widely used in bioinformatics, in the modeling of DNA sequences.

Another recent extension is the triplet Markov model, in which an auxiliary underlying process is added to model some data specificities. Many variants of this model have been proposed. One should also mention the interesting link that has been established between the theory of evidence and the triplet Markov models and which allows to fuse data in Markovian context and to model nonstationary data. Note that alternative multi-stream data fusion strategies have also been proposed in the recent literature, e.g.

Finally, a different rationale towards addressing the problem of modeling nonstationary data by means of hidden Markov models was suggested in 2012. It consists in employing a small recurrent neural network (RNN), specifically a reservoir network, to capture the evolution of the temporal dynamics in the observed data. This information, encoded in the form of a high-dimensional vector, is used as a conditioning variable of the HMM state transition probabilities. Under such a setup, we eventually obtain a nonstationary HMM the transition probabilities of which evolve over time in a manner that is inferred from the data itself, as opposed to some unrealistic ad-hoc model of temporal evolution.

In 2023, two innovative algorithms were introduced for the Hidden Markov Model. These algorithms enable the computation of the posterior distribution of the HMM without the necessity of explicitly modeling the joint distribution, utilizing only the conditional distributions. Unlike traditional methods such as the Forward-Backward and Viterbi algorithms, which require knowledge of the joint law of the HMM and can be computationally intensive to learn, the Discriminative Forward-Backward and Discriminative Viterbi algorithms circumvent the need for the observation's law. This breakthrough allows the HMM to be applied as a discriminative model, offering a more efficient and versatile approach to leveraging Hidden Markov Models in various applications.

The model suitable in the context of longitudinal data is named latent Markov model. The basic version of this model has been extended to include individual covariates, random effects and to model more complex data structures such as multilevel data. A complete overview of the latent Markov models, with special attention to the model assumptions and to their practical use is provided in

Measure theory
Given a Markov transition matrix and an invariant distribution on the states, we can impose a probability measure on the set of subshifts. For example, consider the Markov chain given on the left on the states $$A, B_1, B_2 $$, with invariant distribution $$\pi = (2/7, 4/7, 1/7)   $$. If we "forget" the distinction between $$B_1, B_2$$, we project this space of subshifts on $$A, B_1, B_2 $$ into another space of subshifts on $$A, B  $$, and this projection also projects the probability measure down to a probability measure on the subshifts on $$A, B  $$.

The curious thing is that the probability measure on the subshifts on $$A, B $$ is not created by a Markov chain on $$A, B  $$, not even multiple orders. Intuitively, this is because if one observes a long sequence of $$B^n$$, then one would become increasingly sure that the $$Pr(A | B^n) \to \frac 23  $$, meaning that the observable part of the system can be affected by something infinitely in the past.

Conversely, there exists a space of subshifts on 6 symbols, projected to subshifts on 2 symbols, such that any Markov measure on the smaller subshift has a preimage measure that is not Markov of any order (Example 2.6 ).

Concepts

 * A Revealing Introduction to Hidden Markov Models by Mark Stamp, San Jose State University.
 * Fitting HMM's with expectation-maximization – complete derivation
 * A step-by-step tutorial on HMMs (University of Leeds)
 * Hidden Markov Models (an exposition using basic mathematics)
 * Hidden Markov Models (by Narada Warakagoda)
 * Hidden Markov Models: Fundamentals and Applications Part 1, Part 2 (by V. Petrushin)
 * Lecture on a Spreadsheet by Jason Eisner, Video and interactive spreadsheet
 * Lecture on a Spreadsheet by Jason Eisner, Video and interactive spreadsheet

title: Hidden Markov model


text:
Samariá Gorge

The Samariá Gorge (Φαράγγι Σαμαριάς or just Φάραγγας) is a National Park of Greece since 1962 on the island of Crete – a major tourist attraction of the island – and a World's Biosphere Reserve.

The gorge is in southwest Crete in the regional unit of Chania. It was created by a small river running between the White Mountains (Lefká Óri) and Mt. Volakias. There are a number of other gorges in the White Mountains. While some say that the gorge is 18 km long, this distance refers to the distance between the settlement of Omalos on the northern side of the plateau and the village of Agia Roumeli. In fact, the gorge is 16 km long, starting at an altitude of 1,250 m at the northern entrance, and ending at the shores of the Libyan Sea in Agia Roumeli. The walk through Samaria National Park is 13 km long, but one has to walk another two kilometers to Agia Roumeli from the park exit, making the hike 15 km long. The most famous part of the gorge is the stretch known as the Gates (or, albeit incorrectly, as "Iron Gates"), where the sides of the gorge close in to a width of only four meters and soar up to a height of almost 300 m. The gorge became a national park in 1962, particularly as a refuge for the rare kri-kri (Cretan goat), which is largely restricted to the park and an island just off the shore of Agia Marina. There are several other endemic species in the gorge and surrounding area, as well as many other species of flowers and birds.

The village of Samariá lies just inside the gorge. It was finally abandoned by the last remaining inhabitants in 1962 to make way for the park. The village and the gorge take their names from the village's ancient church, Óssia María.

Visitors to Crete can complete the walk down the gorge from the Omalos plateau to Agia Roumeli on the Libyan Sea, at which point tourists sail to the nearby village of Sougia or Hora Sfakion, where they could spend a night there, or they could catch a coach back to Chania. The walk takes five to seven hours and can be strenuous, especially at the peak of summer.

Local tourist operators provide organized tours to the Gorge. These include bus transportation from one's hotel to the entrance (near Omalos village), and a bus connection that will be waiting for hikers after they disembark the ferry in Sougia or Sfakia (Chora Sfakion). If you are on your own, you can make a one-day round trip from Chania (see below) or from Sougia or Paleochora. Note that the morning buses from Sougia and Paleochora do not operate on Sunday. The ferries leave Agia Roumeli to Chora Sfakion (eastbound) and to Sougia/Paleochora (westbound) at 17:30.

There also exists a way from Agia Roumeli to the Gates and back.

Information provided by the Chania Forest Protection Service

 * Visits to the National park are allowed from May 1 to October 31.
 * Park visiting hours are 07:00 to 15:00 daily. From 15:00 to sunset, visitors are allowed to walk a distance of only two kilometers within the park, either from Xyloskalo or from Agia Roumeli.
 * Within the park it is strictly prohibited to camp, stay overnight, light fires, or swim in the streams of the gorge.

Other gorges in Crete

 * Agia Eirini Gorge
 * Gorge of the Dead
 * Ha Gorge
 * Imbros Gorge
 * Kotsifos Gorge
 * Kourtaliotiko Gorge
 * Richtis Gorge
 * Sarakina Gorge

title: Samariá Gorge


text:
St. Canute's Cathedral

St. Canute's Cathedral (Odense Domkirke or Sankt Knuds Kirke), also known as Odense Cathedral, is named after the Danish king Canute the Saint (Knud den Hellige), otherwise Canute IV. It is a fine example of Brick Gothic architecture. The church's most visited section is the crypt where the remains of Canute and his brother Benedict are on display.

History
St. Canute's Church in one form or another has stood on Abbey Hill in Odense (Klosterbakken) for over 900 years.

Odense was established as the seat of the Bishop of Odense (Othinia) before 988 under the supervision of the Bishop of Schleswig, itself a suffragan of the Archbishops of Hamburg-Bremen. The diocese included the southern Baltic islands of Denmark. The earliest bishops' names have not been recorded. Odense passed to the jurisdiction of Roskilde in 1072 for a short period of time before falling to the Archdiocese of Lund.

The earliest known church on the present location was a travertine church which was reported under construction by Aelnoth of Canterbury, a Benedictine monk at the nearby St. Alban's Priory in 1095. The foundations of the travertine church can still be seen in the crypt of the present building. The church was built in Romanesque style with semi-circular arches supporting a flat timber ceiling. The travertine church was built specifically to house the earthly remains of King Canute, who was murdered in the church of St. Alban's Priory in 1086.

King Canute IV of Denmark
Canute IV of Denmark was one of the sons of King Sven Estridsen. He participated in King Sweyn’s raid of England in 1069. In 1075 he accompanied the Danish fleet on its last great raid of England. When King Sweyn died in 1076, Canute's brother Harald Hen was elected king of Denmark and ruled until his own death in 1080, at which time Canute succeeded him on the throne of Denmark. Canute ruled Denmark from 1080 to 1086.

Canute reigned at a difficult time in Danish history. The idea of a strengthened monarchy did not sit well with the powerful feudal landowners, but it was just what Canute had in mind. After the death of his older brother, the national assembly (Ting) met on Zealand to proclaim Canute king of Denmark. Once the assembly had shouted their approval, Canute stood up and spoke to those assembled, both peasant and nobles: "You called my brother Harald the Whet-stone, but you will learn that I will be hard as granite!" (kampesten). Soon after, he ordered the people of Halland to supply him with horses and wagons to transport himself and his household throughout the kingdom. The assembly met to discuss the king's request. The people decided that the request was not lawful according to the ancient customs and laws they all knew. Canute was enraged by what he heard. "It is your right to hold fast to your rights and laws and bear only the burdens the law allows, but you must also accept that I am free to do with mine what I will, and I forbid you to let your swine graze in Halland's Great Forest which belongs to me!" After hasty consultations the Hallanders supplied the required equipment. Canute did the same in Scania (southern Sweden). At the assembly he required men and supplies to build the new Lund Cathedral. When the assembly baulked, Canute swore he would forbid them to fish in the Øresund. Likewise they too acceded to the king's request.

Canute was a devout Christian and believed that a strong central church in Denmark would give him more power. He was instrumental in improving the nationwide system of bishops by using his own local officials (fogeder) to collect tithes, a new tax, which were used to build the churches, hospitals and monasteries which were just beginning to be introduced into Denmark. Many people were Christian in name, but the old ways were only half-forgotten, and suspicions about foreigners ran high. Peasants were hard-pressed to put food on the table and the forced tithes infuriated peasants, merchants, and nobles alike.

Canute brought about the wrath of some of his chiefs when he hanged Jarl Egil Ragnarsen, his hand-picked governor of Bornholm, and most of his household for piracy. Many nobles took to the seas on occasion looking for a quick way to bolster income. The execution of a high-ranking chief caused more than one chief to reconsider his support for such a troublesome monarch.

Canute's headaches came to a head in 1085-86, when he decided to invade England and try to take the throne from William I who was old and by some reports failing. As a close relative of Canute the Great, Canute's claim was easily as valid as that of William of Normandy's. With the co-operation of Robert I, Count of Flanders, his father-in-law, Canute ordered an armada of 1,000 Danish ships and 60 Norwegian ships to assemble at Struer in the Limfjord, northern Jutland, in the summer of 1085. As had been the tradition since the first Viking raid on England, local chiefs gathered ships, supplies, sailors, and warriors for a share of the profits, loot, slaves, and treasure taken during the raid. Canute in the meantime travelled from assembly to assembly in southern Jutland ordering his people to pay tithes, a tenth of all their produce for the church. At the same time he tried to institute a poll tax (nefgjald) to raise money from the peasants. The tithes and new tax were not well received, and when his brother, Olaf, protested, Canute had him arrested and exiled to Flanders in chains, believing that Olaf was responsible for the growing unrest. It took so long to pacify the south that Canute was unable to come north for weeks. In the meantime, the Danes at Struer were hungry, bored, and very unhappy with the king. Finally they agreed that they would sail home. They told themselves that the weather would turn bad before they could complete a successful raid, and that such a late start would spell disaster. The order to assemble had not been made at an assembly, and was therefore unlawful in the popular view. To the Norwegians they said, "You can stay here in this place of starvation, if you will. We are going home."

When Canute arrived at Struer and found the fleet disbanded, he was furious. He thanked the Norwegians for their patience and sent them home. "Now we (Danes) will play," he said ominously. Canute blamed the wives of several leading chiefs from Jutland of causing bad weather and ordered his officers to collect such heavy fines from their families that it would have bankrupted all of them. Then he proceeded from assembly to assembly to outlaw any man, sailor, peasant or noble who left Struer until they paid a heavy fine, more than the annual income of any but the wealthiest nobles. Within days the peasants in Vendsyssel, the most northern part of Jutland, rebelled. Royal property was burned, and royal officials were tortured and murdered.

The Jute chiefs decided to cast their lot in with the peasants for once, and the rebellion spread rapidly. Canute and his household and other loyal followers fled from Jutland with the intention of returning to Zealand, where Canute had more support. He was convinced instead however to sail over to Funen and then on to the royal farm (gård) at Odense by his trusted adviser, Asbjørn Blak, who also persuaded the king that he could be reconciled with the great landowners and peasants.

Canute and his brothers, Benedict and Erik, and their housecarls went to the king's farm outside Odense. When the peasants and their leaders realized the king was at Odense, they raced to the king's farm, but Canute and Benedict fled into the little timber church of St. Alban's Priory near the river for sanctuary. The rebels refused to recognize sanctuary. "Come out to us, you devil. Too long you have used the edge of your sword to hurt your own people. Now you will feel the edge of our weapons!" Prince Benedict and several others defended the doors. The mob hurled stones and arrows through the windows shouting, "This is for stealing my cow! This is for taking my horses!" Since they couldn't get through the heavy outer doors, the mob tried to set fire to the church, but a light rain kept the fire from taking hold. They began tearing at the timber walls to get access. Prince Benedict shouted, "It would be better that you go home to thresh your grain than stand here and exchange blows with the king's men!" The remaining defenders retreated to the choir door which separated the altar area from the nave of the church. The floors ran with blood. "There he is!" shouted Blak, but before the traitor could move against the king, he was slain by Prince Benedict. The mob hacked Prince Benedict to death. Canute had received communion and tradition says he offered no resistance when he was killed at the main altar. Forensic evidence suggests he was speared from the front and had his skull smashed, perhaps by a stone thrown through an opening that had been torn through the wall of the choir. Prince Erik, later King Erik Ejegod, managed to talk his way out of the king's farm and fled to Zealand and then with his wife and child to Skania. The seventeen housecarls loyal to Canute were massacred within the confines of the church on 10 July 1086.

The Benedictine monks buried Canute and Prince Benedict in front of the main altar of the priory church. The story of Canute's death at the altar and his well-known devoutness quickly caught the popular imagination. When his queen, Adela of Flanders, came to move her husband's body to Flanders, a bright light shone around St. Alban's church. The queen left her husband where he was, and the faithful streamed to the church which housed the remains of their saintly king. Almost immediately there were reports of miraculous healings at the site of his burial. Blind, deaf, and lame were healed. Seven years of famine following Canute's death were another sure sign that Canute was worthy of veneration. His brother and successor, Olaf I, was given the nickname Hunger because he was unable to do anything about the famine that ravaged Denmark for years after Canute's death.

The unique circumstances of Canute's death was seized upon by the Roman Catholic Church as an example of saintliness for the newly converted peoples of Scandinavia. Canute was canonized in 1101 by Pope Pascal II. Some confusion exists among writers about the location of the new St. Canute's church thinking it rose on the site of the assassination, but St Alban's and St Canute's churches were not in the same location. The new cathedral, constructed in imported tuff stone, was constructed a little farther from the river, and was well underway before Canute became a saint. Aelnoth of Canterbury, an English monk, reported the building of St Canute's in 1095 and described the miracles reported at the site of Canute's grave. When the first church of St. Canute was completed, a three-day fast was proclaimed for the entire kingdom and the remains of Canute and Benedict were moved to the cathedral. It was believed that if the king was truly a saint that the shroud should be set on fire and the body would not be harmed. The shroud of Saint Canute was set alight, and the fire indeed left no mark upon the body of the king.

Second St. Canute's Cathedral
During the civil war between Eric IV and his brother, Abel, Odense and the cathedral were burned down in 1247. The present church was constructed in several phases to replace the aging and inadequate stone church in about 1300 by Bishop Gisico (1287–1300). The new cathedral was built in Gothic style with its typical pointed arches and high vaulted ceilings. The building material of choice for the time was oversized red brick which was cheaper and easier to work with than the porous stone available. Portions of the stone cathedral were taken down and the new building expanded around the old.

In all it took approximately two hundred years to complete the cathedral, which was finally dedicated on 30 April 1499. The church was built in cruciform shape without a tower. The ancient crypt was expanded in such a way that pilgrims could visit the reliquary of Saint Canute beneath the raised choir without interfering with the canons' hourly services above. The canons also claimed they had relics of Saint Alban which Canute supposedly stole on his 1075 attack on Ely, England.

King Hans of Denmark (d. 1513) was buried in the cathedral in 1513. His wife, Christina of Saxony, who lived the latter part of her life in a nunnery in Odense, commissioned the famous German sculptor Claus Berg to create a magnificent burial chapel in the church of the Franciscan friary in Odense, where both she and her husband were laid to rest after her death in 1521. The son of Hans and Christina, King Christian II, with his wife Isabella of Austria and their son Hans, were exhumed from St. Peter's Abbey in Ghent, Belgium and reinterred in the royal family chapel in 1883. In 1807 the former Franciscan church was demolished, and Claus Berg's magnificent late Gothic altarpiece and the bodies of the four royals were transferred to St. Canute's Cathedral. The altarpiece is truly one of Denmark's national treasures. It was carved between 1515 and 1525. Each of the three sections is intricately carved and gilded. It survived the iconoclastic fervour of the Reformation perhaps because of its connection with the royal burials.

The single tower was completed in 1586 over the west entrance in the same style as the rest of the cathedral. Five bells hang in the tower, the oldest from 1300 cast by Adam..., one cast in 1597 by Jens Hansen, a bell cast in 1677, and one from 1767 cast by ... Leitze. The most recent bell was cast by MP Allerup in 1880.

In 1633 Valkendorf's Chapel was added, by all accounts a fine example of Renaissance artistry. Unfortunately it was dismantled in the great restoration of 1868.

Thomas Kingo was made the Lutheran Bishop of Odense in 1634. He was Denmark's most famous psalmist and produced a new hymnbook to which he personally contributed 85 hymns.

1752 Amdie Worm's spectacular organ was installed. The facade of the organ remains, but the organ has been expanded and improved to become the cherished voice of Odense Cathedral.

During restoration work in the 1870s, the crypt which had been closed since the Reformation was refurbished and opened as a chapel, and Saint Canute once more went on display.

Other sources

 * Hvidtfeldt, Arild (1985) Danmarks Riges Krønike (København: Peter Asschenfeldt's Bogklub) ISBN 9788773655870

title: St. Canute's Cathedral


text:
Natural language understanding

Natural language understanding (NLU) or natural language interpretation (NLI) is a subset of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU has been considered an AI-hard problem.

There is considerable commercial interest in the field because of its application to automated reasoning, machine translation, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.

History
The program STUDENT, written in 1964 by Daniel Bobrow for his PhD dissertation at MIT, is one of the earliest known attempts at NLU by a computer. Eight years after John McCarthy coined the term artificial intelligence, Bobrow's dissertation (titled Natural Language Input for a Computer Problem Solving System) showed how a computer could understand simple natural language input to solve algebra word problems.

A year later, in 1965, Joseph Weizenbaum at MIT wrote ELIZA, an interactive program that carried on a dialogue in English on any topic, the most popular being psychotherapy. ELIZA worked by simple parsing and substitution of key words into canned phrases and Weizenbaum sidestepped the problem of giving the program a database of real-world knowledge or a rich lexicon. Yet ELIZA gained surprising popularity as a toy project and can be seen as a very early precursor to current commercial systems such as those used by Ask.com.

In 1969, Roger Schank at Stanford University introduced the conceptual dependency theory for NLU. This model, partially influenced by the work of Sydney Lamb, was extensively used by Schank's students at Yale University, such as Robert Wilensky, Wendy Lehnert, and Janet Kolodner.

In 1970, William A. Woods introduced the augmented transition network (ATN) to represent natural language input. Instead of phrase structure rules ATNs used an equivalent set of finite state automata that were called recursively. ATNs and their more general format called "generalized ATNs" continued to be used for a number of years.

In 1971, Terry Winograd finished writing SHRDLU for his PhD thesis at MIT. SHRDLU could understand simple English sentences in a restricted world of children's blocks to direct a robotic arm to move items. The successful demonstration of SHRDLU provided significant momentum for continued research in the field. Winograd continued to be a major influence in the field with the publication of his book Language as a Cognitive Process. At Stanford, Winograd would later advise Larry Page, who co-founded Google.

In the 1970s and 1980s, the natural language processing group at SRI International continued research and development in the field. A number of commercial efforts based on the research were undertaken, e.g., in 1982 Gary Hendrix formed Symantec Corporation originally as a company for developing a natural language interface for database queries on personal computers. However, with the advent of mouse-driven graphical user interfaces, Symantec changed direction. A number of other commercial efforts were started around the same time, e.g., Larry R. Harris at the Artificial Intelligence Corporation and Roger Schank and his students at Cognitive Systems Corp. In 1983, Michael Dyer developed the BORIS system at Yale which bore similarities to the work of Roger Schank and W. G. Lehnert.

The third millennium saw the introduction of systems using machine learning for text classification, such as the IBM Watson. However, experts debate how much "understanding" such systems demonstrate: e.g., according to John Searle, Watson did not even understand the questions.

John Ball, cognitive scientist and inventor of the Patom Theory, supports this assessment. Natural language processing has made inroads for applications to support human productivity in service and e-commerce, but this has largely been made possible by narrowing the scope of the application. There are thousands of ways to request something in a human language that still defies conventional natural language processing. According to Wibe Wagemans, "To have a meaningful conversation with machines is only possible when we match every word to the correct meaning based on the meanings of the other words in the sentence – just like a 3-year-old does without guesswork."

Scope and context
The umbrella term "natural language understanding" can be applied to a diverse set of computer applications, ranging from small, relatively simple tasks such as short commands issued to robots, to highly complex endeavors such as the full comprehension of newspaper articles or poetry passages. Many real-world applications fall between the two extremes, for instance text classification for the automatic analysis of emails and their routing to a suitable department in a corporation does not require an in-depth understanding of the text, but needs to deal with a much larger vocabulary and more diverse syntax than the management of simple queries to database tables with fixed schemata.

Throughout the years various attempts at processing natural language or English-like sentences presented to computers have taken place at varying degrees of complexity. Some attempts have not resulted in systems with deep understanding, but have helped overall system usability. For example, Wayne Ratliff originally developed the Vulcan program with an English-like syntax to mimic the English speaking computer in Star Trek. Vulcan later became the dBase system whose easy-to-use syntax effectively launched the personal computer database industry. Systems with an easy to use or English-like syntax are, however, quite distinct from systems that use a rich lexicon and include an internal representation (often as first order logic) of the semantics of natural language sentences.

Hence the breadth and depth of "understanding" aimed at by a system determine both the complexity of the system (and the implied challenges) and the types of applications it can deal with. The "breadth" of a system is measured by the sizes of its vocabulary and grammar. The "depth" is measured by the degree to which its understanding approximates that of a fluent native speaker. At the narrowest and shallowest, English-like command interpreters require minimal complexity, but have a small range of applications. Narrow but deep systems explore and model mechanisms of understanding, but they still have limited application. Systems that attempt to understand the contents of a document such as a news release beyond simple keyword matching and to judge its suitability for a user are broader and require significant complexity, but they are still somewhat shallow. Systems that are both very broad and very deep are beyond the current state of the art.

Components and architecture
Regardless of the approach used, most NLU systems share some common components. The system needs a lexicon of the language and a parser and grammar rules to break sentences into an internal representation. The construction of a rich lexicon with a suitable ontology requires significant effort, e.g., the Wordnet lexicon required many person-years of effort.

The system also needs theory from semantics to guide the comprehension. The interpretation capabilities of a language-understanding system depend on the semantic theory it uses. Competing semantic theories of language have specific trade-offs in their suitability as the basis of computer-automated semantic interpretation. These range from naive semantics or stochastic semantic analysis to the use of pragmatics to derive meaning from context. Semantic parsers convert natural-language texts into formal meaning representations.

Advanced applications of NLU also attempt to incorporate logical inference within their framework. This is generally achieved by mapping the derived meaning into a set of assertions in predicate logic, then using logical deduction to arrive at conclusions. Therefore, systems based on functional languages such as Lisp need to include a subsystem to represent logical assertions, while logic-oriented systems such as those using the language Prolog generally rely on an extension of the built-in logical representation framework.

The management of context in NLU can present special challenges. A large variety of examples and counter examples have resulted in multiple approaches to the formal modeling of context, each with specific strengths and weaknesses.

title: Natural language understanding


text:
Talk:Montgomery County, Maryland

Proposed merge of Montgomery County Sheriff's Office (Maryland) into Montgomery County, Maryland
The Montgomery County Sheriff's Office does not appear to be notable on its own. Although the present page has a decent number of references, none appear to constitute significant coverage, and the majority are primary first-party sources. Most of the page content is picayune and not encyclopedic, consisting of trivia about the department's equipment and lists of nonnotable sheriffs. I was actually a little surprised by this one considering the size of Montgomery County, but maybe the Montgomery County Police Department is the important law enforcement agency in the county? Apocheir (talk) 01:13, 21 December 2021 (UTC)

Opposing view: I think the Montgomery County Sheriff's Office page should remain its own entry, regardless of size of the agency or article length. Maybe the article itself can be improved. It is true that the same county's police department is better funded and is the primary law enforcement agency in Montgomery County, but that alone should not justify killing the Sheriff's Office page on Wikipedia. (talk) 00:05, 23 December 2021 (UTC)

 * Nobody is proposing to "kill" anything. The proposal is to merge this page into the county's page, preserving that content which is encyclopedic and verifiable. No organization is inherently notable, and this sheriff's office is not an exception. -Apocheir (talk) 20:01, 28 December 2021 (UTC)

 * The agency was founded a year after the republic, in 1777. The article is (relatively) well-written, and is not spam or self-congratulatory. WP:IAR would seem to apply here. Remove the tag, keep the article, and move on to more pressing issues in this project. Too much time is spent on doing things just for the sake of doing them because it doesn't strictly meet WP guidelines. Lucky for us WP:IAR saves us from nonsense such as this. It's me...Sallicio!$\color{Red} \oplus$ 15:21, 19 January 2022 (UTC)

 * I really don't think IAR means you get to ignore the entire verifiability policy. It's also not helpful to call good-faith efforts towards increasing the quality of this encyclopedia "nonsense". -Apocheir (talk) 23:47, 19 January 2022 (UTC)

 * No one is suggesting to "ignore" the entire policy (and I'm pretty sure you already know this). And actually, IAR is exactly for instances like this. IAR is to promote a modicum of common sense and judiciousness when editing. This is a LE agency that is as old as the nation itself. I don't have time to save articles like I used to, but I am pretty sure one could find plenty of "verifiability policy" information on the net. I could be wrong, but if one really wanted to "increase the quality" of the project, one would transpose every bit of includable information from the article one was blanking to the new article (including relevant photos and infoboxes). Based on your history of "merging", you don't do much work beyond blanking pages, redirecting, and adding a few passing sentences about the original article. And you do this at a rate that would rival the world's fastest supercomputer. Because of this, it seems you are more concerned with increasing your edit count than the quality of the project. There is a lot of good information being lost when one doesn't responsibly merge articles. I make no bones that I am an inclusionist; but do as you will: delete it, merge it, or do whatever you want. (Note: Just so you know, I really don't think you're a "bad editor". But I do think you are not taking the time to properly merge articles, and not nearly enough time to think "Is what I am doing actually making the project better?" And I'm not saying all your merge suggestions are wrong. Your heart is in the right spot. Just slow down. Way down.) It's me...Sallicio!$\color{Red} \oplus$ 00:44, 20 January 2022 (UTC)
 * I am not trying to inflate my edit count, as you keep accusing me of. This would be a very inefficient way of doing that, for one thing. If that was all I wanted to do I'd go fix typos or something like that.
 * Removing material that is biased, unverifiable, or not notable improves Wikipedia. Despite your assertions to the contrary, the article's citations almost entirely consist of content produced by the MCSO or bodies closely related to it. Practically no organization is an unbiased source for information about itself. The article cites posts on their Facebook page, for crying out loud!
 * Since you keep pointing to the department's age, I would also mention that 1777 is not old for a sheriff's department in the US. The Albany County Sheriff's Department was formed in the 1660s. Prince George's County Sheriff's Office was formed in 1696, so the MCSO isn't old compared to other sheriff's offices in Maryland. Like most sheriff's departments in the US it was created shortly after the creation of the county it serves. There's a difference between "old" and "historic".
 * If you have an interest in sheriff's offices in Maryland, you are in the best place to find reliable sources to establish notability for this page. Instead you've just been saying that the rules don't apply to you and cheerfully accusing me of bad editing practices. I don't know how to have a discussion with someone who refuses to even acknowledge that the rules might be useful guidance. Apocheir (talk) 20:41, 27 February 2022 (UTC)

 * Keep leave it as it is. --evrik (talk) 22:18, 27 February 2022 (UTC)
 * Support merge The sheriff's department article isn't really an encyclopedia article, it's a handful of lists that all vaguely relate to the same thing. The individual sheriffs are NN and are not going to become notable, and it's not clear what encyclopedic value is served by a long list of names of people about whom nothing interesting can be said; there's a single source for essentially the entire list, which would make an appropriate EL or reference in the merged article.  The history section has more space devoted to sub-headings than to actual content; the duties are trivial content (shocker, it's a sheriff's office!) and mostly unsourced; and the sourcing in the Equipment section is farcical.  Trim out all the junk and what's left would make a very nice section in the article on the county. --JBL (talk) 12:04, 17 April 2022 (UTC)

Proposed merge of Montgomery County Council (Maryland) into Montgomery County, Maryland
Anything that would go on the page of the Council is either already on the main County's page or the List of Councilmembers page. While it is notable on its own, it would also just be the same thing that's already listed elsewhere. Oath2order (talk) 14:20, 6 June 2022 (UTC)
 * I started Montgomery County Council (Maryland) with the notion that there might be council history that would be relevant on the council's page but not really on the county page. I also would propose that the merge should be List of Councilmembers into the Council page. But I certainly haven't done anything about it since, and it is a rather stubby page, so I won't object if people want to merge the Council page into the County page. PRRfan (talk) 20:30, 6 June 2022 (UTC)
 * A thought: how about merging List of members of MCC into the MCC page? PRRfan (talk) 21:07, 7 June 2022 (UTC)
 * I like that idea, it keeps it the same. But what else would be on the MCC page? Just copy and paste the history from here to there? Oath2order (talk) 19:31, 10 June 2022 (UTC)
 * That's what I'm thinking. Put it under the existing intro ("The Montgomery County Council is the legislative branch of the government of Montgomery County in the U.S. state of Maryland. It has nine members who serve four-year terms. All are elected at the same time by the voters of Montgomery County"). Eventually, I would think, we'd add more council history and information about its processes between the intro and the list of members. PRRfan (talk) 19:39, 10 June 2022 (UTC)
 * Should be merged with List of members of the Montgomery County Council (Maryland), I feel. – Illegitimate Barrister (talk • contribs), 03:52, 27 June 2022 (UTC)
 * How come, and what do you think of the above stuff about List of into MCC? Oath2order (talk) 06:37, 3 July 2022 (UTC)

County nickname
I had long heard that the county's nickname was "Monkey County," especially when goveernment or their officials did something especially incomprehensible to residents. Montgomery Wards department store was also nicknamed Monkey Wards, for those of you over 50 years of age and remember that! The statement that the county's nickname is "MoCo" well, I mean I've heard that recently, but the source material is a poll taken for naming a bus in 2015 and the county executive's endorsement of a hashtag (#) MoCo. Neither of these sources scream, "origin of an official nickname" to me. StarHOG (Talk) 21:30, 8 August 2023 (UTC)
 * I've lived in MoCo for at least a decade and have never heard "Monkey County". I'm also relatively active on the subreddit, also never heard that there and believe me there are complaints when the officials do things. Oath2order (talk) 21:40, 8 August 2023 (UTC)
 * Someone mentioned to me that it was so common that the department store, Montgomery Ward, was also referred to as Monkey Wards. StarHOG (Talk) 16:09, 22 August 2023 (UTC)

title: Talk:Montgomery County, Maryland


text: Talk:Delilah and the Space Rigger

title: Talk:Delilah and the Space Rigger