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| In [[probability theory]] and [[statistics]], given two jointly distributed [[random variable]]s ''X'' and ''Y'', the '''conditional probability distribution''' of ''Y'' given ''X'' is the [[probability distribution]] of ''Y'' when ''X'' is known to be a particular value; in some cases the conditional probabilities may be expressed as functions containing the unspecified value ''x'' of ''X'' as a parameter. The conditional distribution contrasts with the [[marginal distribution]] of a random variable, which is its distribution without reference to the value of the other variable.
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| If the conditional distribution of ''Y'' given ''X'' is a [[continuous distribution]], then its [[probability density function]] is known as the '''conditional density function'''. The properties of a conditional distribution, such as the [[Moment (mathematics)|moments]], are often referred to by corresponding names such as the [[conditional mean]] and [[conditional variance]].
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| More generally, one can refer to the conditional distribution of a subset of a set of more than two variables; this conditional distribution is contingent on the values of all the remaining variables, and if more than one variable is included in the subset then this conditional distribution is the conditional [[joint distribution]] of the included variables.
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| ==Discrete distributions==
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| For [[discrete random variable]]s, the [[conditional probability]] mass function of ''Y'' given the occurrence of the value ''x'' of ''X'' can be written according to its definition as:
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| :<math>p_Y(y\mid X = x)=P(Y = y \mid X = x) = \frac{P(X=x\ \cap Y=y)}{P(X=x)}.</math>
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| Due to the occurrence of <math>P(X=x)</math> in a denominator, this is defined only for non-zero (hence strictly positive) <math>P(X=x).</math>
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| The relation with the probability distribution of ''X'' given ''Y'' is: | |
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| :<math>P(Y=y \mid X=x) P(X=x) = P(X=x\ \cap Y=y) = P(X=x \mid Y=y)P(Y=y).</math>
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| ==Continuous distributions==
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| Similarly for [[continuous random variable]]s, the conditional [[probability density function]] of ''Y'' given the occurrence of the value ''x'' of ''X'' can be written as
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| :<math>f_Y(y \mid X=x) = \frac{f_{X, Y}(x, y)}{f_X(x)}, </math> | |
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| where ''f<sub>X,Y''</sub>(''x, y'') gives the [[joint distribution|joint density]] of ''X'' and ''Y'', while ''f<sub>X''</sub>(''x'') gives the [[marginal density]] for ''X''. Also in this case it is necessary that <math>f_X(x)>0</math>.
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| The relation with the probability distribution of ''X'' given ''Y'' is given by:
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| :<math>f_Y(y \mid X=x)f_X(x) = f_{X,Y}(x, y) = f_X(x \mid Y=y)f_Y(y). </math>
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| The concept of the conditional distribution of a continuous random variable is not as intuitive as it might seem: [[Borel's paradox]] shows that conditional probability density functions need not be invariant under coordinate transformations.
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| ==Relation to independence==
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| Random variables ''X'', ''Y'' are [[Statistical independence|independent]] if and only if the conditional distribution of ''Y'' given ''X'' is, for all possible realizations of ''X'', equal to the unconditional distribution of ''Y''. For discrete random variables this means ''P''(''Y'' = ''y'' | ''X'' = ''x'') = ''P''(''Y'' = ''y'') for all relevant ''x'' and ''y''. For continuous random variables ''X'' and ''Y'', having a [[joint density function]], it means ''f''<sub>''Y''</sub>(''y'' | ''X=x'') = ''f''<sub>''Y''</sub>(''y'') for all relevant x and y.
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| ==Properties== | |
| Seen as a function of ''y'' for given ''x'', ''P''(''Y'' = ''y'' | ''X'' = ''x'') is a probability and so the sum over all ''y'' (or integral if it is a conditional probability density) is 1. Seen as a function of ''x'' for given ''y'', it is a [[likelihood function]], so that the sum over all ''x'' need not be 1.
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| ==Measure-Theoretic Formulation==
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| Let <math>(\Omega, \mathcal{F}, P)</math> be a probability space, <math>\mathcal{G} \subseteq \mathcal{F}</math> a <math>\sigma</math>-field in <math>\mathcal{F}</math>, and <math>X : \Omega \to \mathbb{R}</math> a real-valued random variable (measurable with respect to the Borel <math>\sigma</math>-field <math>\mathcal{R}^1</math> on <math>\mathbb{R}</math>). It can be shown that there exists<ref>[[#billingsley95|Billingsley (1995)]], p. 439</ref> a function <math>\mu : \mathcal{R}^1 \times \Omega \to \mathbb{R}</math> such that <math>\mu(\cdot, \omega)</math> is a probability measure on <math>\mathcal{R}^1</math> for each <math>\omega \in \Omega</math> (i.e., it is [[Regular_conditional_probability|'''regular''']]) and <math>\mu(H, \cdot) = P(X \in H | \mathcal{G})</math> (almost surely) for every <math>H \in \mathcal{R}^1</math>. For any <math>\omega \in \Omega</math>, the function <math>\mu(\cdot, \omega) : \mathcal{R}^1 \to \mathbb{R}</math> is called a '''[[Conditional_expectation#Definition_of_conditional_probability|conditional probability]] distribution''' of <math>X</math> given <math>\mathcal{G}</math>. In this case,
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| :<math>E[X | \mathcal{G}] = \int_{-\infty}^\infty x \, \mu(d x, \cdot)</math>
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| almost surely.
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| == See also ==
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| *[[Conditioning (probability)]]
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| *[[Conditional probability]]
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| *[[Regular conditional probability]]
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| ==Notes==
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| {{reflist}}
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| ==References==
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| *{{cite book
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| | author = [[Patrick Billingsley]]
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| | title = Probability and Measure, 3rd ed.
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| | publisher = John Wiley and Sons
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| | location = New York, Toronto, London
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| | year = 1995
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| | ref = billingsley95}}
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| [[Category:Probability theory]]
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| [[Category:Types of probability distributions]]
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