141.211.98.76 at 06:21, 18 August 2014

2014-08-18T06:21:07Z

← Older revision		Revision as of 08:21, 18 August 2014
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	~~{{Refimprove\|date=December 2013}}~~		Radio Journalist Carmouche from Sainte-Catherine, has many hobbies that include interior design, [http://www.pierrecardinhali.de/?option=com_k2&view=itemlist&task=user&id=98664 property search singapore] developers in singapore and tombstone rubbing. Last year very recently completed a journey Historic Centre of Ceský Krumlov.
	~~{{orphan\|date=December 2013}}~~
	~~In [[statistics]]~~, ~~the '''conditional probability table (CPT)''' is defined for a set of discrete (not independent)~~ [~~[random variable]]s to demonstrate [[marginal probability]] of a single variable with respect to the others. For example, assume there are three random variables <math>x_1,x_2, x_3<~~/~~math> where each have <math>K<~~/~~math> states~~. ~~Then, the conditional probability table of <math>x_1</math> provides the marginal probability values for <math>P(x_1\mid x_2,x_3)</math>~~. ~~Clearly, this table has ''K''<sup>3<~~/~~sup> cells. In general, for <math>M</math> number of variables <math>x_1,x_2,\ldots,x_M</math> with <math>K</math> states, the CPT has size~~&~~nbsp;''K''<sup>''M''</sup>.<ref name~~=~~murphybook>{{cite book\|last~~=~~Murphy\|first~~=~~KP\|title=Machine learning: a probabilistic perspective\|~~year~~=2012\|publisher=The MIT Press}}</ref>~~

	~~CPT table can be put into~~ a ~~matrix form. For example, the values~~ of ~~<math>P(x_j\mid x_i)=T_{ij}</math> create a matrix. This matrix is [[stochastic matrix]] since its row sum is equals to 1; i.e~~. ~~<math>\sum_j T_{ij}</math>.~~

	~~==References==~~
	~~{{Reflist}}~~

	~~{{Statistics-stub}}~~
	~~[[Category:Probability theory]]~~
	~~[[Category:Conditionals]]~~

en>Enyokoyama: /* References */ the paper would be commented out.

2013-12-19T13:54:02Z

References: the paper would be commented out.

New page

{{Refimprove|date=December 2013}}
{{orphan|date=December 2013}}
In [[statistics]], the '''conditional probability table (CPT)''' is defined for a set of discrete (not independent) [[random variable]]s to demonstrate [[marginal probability]] of a single variable with respect to the others. For example, assume there are three random variables <math>x_1,x_2, x_3</math> where each have <math>K</math> states. Then, the conditional probability table of <math>x_1</math> provides the marginal probability values for <math>P(x_1\mid x_2,x_3)</math>. Clearly, this table has ''K''<sup>3</sup> cells. In general, for <math>M</math> number of variables <math>x_1,x_2,\ldots,x_M</math> with <math>K</math> states, the CPT has size ''K''<sup>''M''</sup>.<ref name=murphybook>{{cite book|last=Murphy|first=KP|title=Machine learning: a probabilistic perspective|year=2012|publisher=The MIT Press}}</ref>

CPT table can be put into a matrix form. For example, the values of <math>P(x_j\mid x_i)=T_{ij}</math> create a matrix. This matrix is [[stochastic matrix]] since its row sum is equals to 1; i.e. <math>\sum_j T_{ij}</math>.

==References==
{{Reflist}}

{{Statistics-stub}}
[[Category:Probability theory]]
[[Category:Conditionals]]

Cardy formula - Revision history

141.211.98.76 at 06:21, 18 August 2014

en>Enyokoyama: /* References */ the paper would be commented out.