Revising opinions in statistics - Revision history

wikademia>Wikademia: 12 revisions

2009-06-05T20:19:18Z

12 revisions

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wikademia>Baccyak4H: AfD: Nominated for deletion; see Wikipedia:Articles for deletion/Revising opinions in statistics

2009-06-05T17:59:28Z

AfD: Nominated for deletion; see Wikipedia:Articles for deletion/Revising opinions in statistics

← Older revision		Revision as of 17:59, 5 June 2009
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			{{AfDM\|page=Revising opinions in statistics\|logdate=2009 June 5\|substed=yes }}
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	Researchers who use [[personal probability]] can proceed as follows:		Researchers who use [[personal probability]] can proceed as follows:

wikademia>DavidCBryant: /* See also */ Removed reference to "applied statistics", which is being merged out of existence.

2007-02-11T19:31:03Z

See also: Removed reference to "applied statistics", which is being merged out of existence.

← Older revision		Revision as of 19:31, 11 February 2007
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	==See also==		==See also==
	*[[~~statistical~~ theory]]		*[[Statistical theory]]
	*[[~~applied statistics~~]]		*[[Statistics]]

	[[Category:Bayesian statistics]]		[[Category:Bayesian statistics]]

wikademia>Den fjättrade ankan at 11:33, 18 August 2006

2006-08-18T11:33:02Z

← Older revision		Revision as of 11:33, 18 August 2006
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	==See also==		==See also==

	*[[statistical theory]]		*[[statistical theory]]
	*[[applied statistics]]		*[[applied statistics]]

	[[Category:~~Statistics~~]]		[[Category:Bayesian statistics]]

wikademia>CesarB: Category:Statistics; fmt see also

2005-05-07T03:26:23Z

Category:Statistics; fmt see also

← Older revision		Revision as of 03:26, 7 May 2005
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	==See also==		==See also==

	[[statistical theory]] -- [[applied statistics]].		*[[statistical theory]]
			*[[applied statistics]]

			[[Category:Statistics]]

216.153.143.214: typo (distribtion -> distribution)

2005-01-21T20:33:54Z

typo (distribtion -> distribution)

← Older revision		Revision as of 20:33, 21 January 2005
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	*A [[statistical model]] for the data generating process is assumed. The model might specify that the data follows a normal distribution with an unknown mean.		*A [[statistical model]] for the data generating process is assumed. The model might specify that the data follows a normal distribution with an unknown mean.

	*The researcher describes his opinion of a [[prior probability\|prior]] distribution for the unknown [[parameter]]s of the model. So the prior ~~distribtion~~ of the unknown mean might be a [[normal distribution]] centered at 10 with a [[standard deviation]] of 2.		*The researcher describes his opinion of a [[prior probability\|prior]] distribution for the unknown [[parameter]]s of the model. So the prior distribution of the unknown mean might be a [[normal distribution]] centered at 10 with a [[standard deviation]] of 2.

	*The data is then observed, and with the [[likelihood function]] of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [[Bayes' theorem]]) the appropriate opinion consistent with both sources of information. This is called the [[posterior probability\|posterior]] distribution.		*The data is then observed, and with the [[likelihood function]] of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [[Bayes' theorem]]) the appropriate opinion consistent with both sources of information. This is called the [[posterior probability\|posterior]] distribution.

wikademia>Henrygb at 16:24, 1 July 2004

2004-07-01T16:24:47Z

← Older revision		Revision as of 16:24, 1 July 2004
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	*A [[statistical model]] for the data generating process is assumed. The model might specify that the data follows a normal distribution with an unknown mean.		*A [[statistical model]] for the data generating process is assumed. The model might specify that the data follows a normal distribution with an unknown mean.

	*The researcher describes his opinion ~~about~~ the unknown mean ~~as having~~ a [[normal distribution]] centered at 10 with a [[standard deviation]] of 2~~. This would be called the researcher's '''[[prior probability\|prior]]''' distribution for the mean~~.		*The researcher describes his opinion of a [[prior probability\|prior]] distribution for the unknown [[parameter]]s of the model. So the prior distribtion of the unknown mean might be a [[normal distribution]] centered at 10 with a [[standard deviation]] of 2.

	*~~With~~ the [[likelihood function]] of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [[Bayes' theorem]]) the appropriate opinion consistent with both sources of information. This is called the ~~'''~~[[posterior probability\|posterior]]~~'''~~ distribution.		*The data is then observed, and with the [[likelihood function]] of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [[Bayes' theorem]]) the appropriate opinion consistent with both sources of information. This is called the [[posterior probability\|posterior]] distribution.

	==See also==		==See also==

	[[statistical theory]] -- [[applied statistics]].		[[statistical theory]] -- [[applied statistics]].

wikademia>Michael Hardy: Putting the number "1" in front of each paragraph does not make sense. These "#" symbols do successive numbering ONLY when they're all in the same paragraph! Don't say "back to" it is presumptuous.

2004-06-09T19:09:30Z

Putting the number "1" in front of each paragraph does not make sense. These "#" symbols do successive numbering ONLY when they're all in the same paragraph! Don't say "back to" it is presumptuous.

← Older revision		Revision as of 19:09, 9 June 2004
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	*With the [[likelihood function]] of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [[Bayes' theorem]]) the appropriate opinion consistent with both sources of information. This is called the '''[[posterior probability\|posterior]]''' distribution.		*With the [[likelihood function]] of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [[Bayes' theorem]]) the appropriate opinion consistent with both sources of information. This is called the '''[[posterior probability\|posterior]]''' distribution.

	~~Back to~~ [[statistical theory]] -- [[applied statistics]].		==See also==

			[[statistical theory]] -- [[applied statistics]].

wikademia>Michael Hardy: Putting the number "1" in front of each paragraph does not make sense. These "#" symbols do successive numbering ONLY when they're all in the same paragraph!

2004-06-09T19:08:46Z

Putting the number "1" in front of each paragraph does not make sense. These "#" symbols do successive numbering ONLY when they're all in the same paragraph!

← Older revision		Revision as of 19:08, 9 June 2004
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	Researchers who use [[personal probability]] can proceed as follows:		Researchers who use [[personal probability]] can proceed as follows:

	#A [[statistical model]] for the data generating process is assumed. The model might specify that the data follows a normal distribution with an unknown mean.		*A [[statistical model]] for the data generating process is assumed. The model might specify that the data follows a normal distribution with an unknown mean.

	#The researcher describes his opinion about the unknown mean as having a [[normal distribution]] centered at 10 with a [[standard deviation]] of 2. This would be called the researcher's '''[[prior probability\|prior]]''' distribution for the mean.		*The researcher describes his opinion about the unknown mean as having a [[normal distribution]] centered at 10 with a [[standard deviation]] of 2. This would be called the researcher's '''[[prior probability\|prior]]''' distribution for the mean.

	#With the [[likelihood function]] of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [[Bayes' theorem]]) the appropriate opinion consistent with both sources of information. This is called the '''[[posterior probability\|posterior]]''' distribution.		*With the [[likelihood function]] of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [[Bayes' theorem]]) the appropriate opinion consistent with both sources of information. This is called the '''[[posterior probability\|posterior]]''' distribution.

	Back to [[statistical theory]] -- [[applied statistics]].		Back to [[statistical theory]] -- [[applied statistics]].

wikademia>Henrygb: links

2004-06-09T18:13:39Z

links

← Older revision		Revision as of 18:13, 9 June 2004
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	#A [[statistical model]] for the data generating process is assumed. The model might specify that the data follows a normal distribution with an unknown mean.		#A [[statistical model]] for the data generating process is assumed. The model might specify that the data follows a normal distribution with an unknown mean.

	#The researcher describes his opinion about the unknown mean as having a [[normal distribution]] centered at 10 with a [[standard deviation]] of 2. This would be called the researcher's '''prior''' distribution for the mean.		#The researcher describes his opinion about the unknown mean as having a [[normal distribution]] centered at 10 with a [[standard deviation]] of 2. This would be called the researcher's '''[[prior probability\|prior]]''' distribution for the mean.

	#With the [[likelihood function]] of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [[Bayes' theorem]]) the appropriate opinion consistent with both sources of information. This is called the '''posterior''' distribution.		#With the [[likelihood function]] of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [[Bayes' theorem]]) the appropriate opinion consistent with both sources of information. This is called the '''[[posterior probability\|posterior]]''' distribution.

	Back to [[statistical theory]] -- [[applied statistics]].		Back to [[statistical theory]] -- [[applied statistics]].