Conditional distribution

A conditional distribution is the probability law of one random quantity given information about another.

The probability mass function of a discrete conditional distribution. The elementary ratio requires $p_X(x)>0$; a value of zero gives no event-conditioning definition through this ratio.

A regular conditional distribution of a random variable $X$ given $Y$ is a Markov kernel $K$ such that, for all measurable sets $A,B$,For each $y$, $K(y,\cdot )$ is a probability measure; for each $B$, $K(\cdot ,B)$ is measurable. Thus it assigns an actual probability distribution to a specified observation even when $P(Y=y)=0$. Such kernels exist for standard Borel state spaces, including real-valued variables, and their uniqueness is only up to $P_Y$-null sets. With a joint density and positive marginal density, the ratio of the two densities defines the usual conditional density kernel. Values outside the marginal support or on a null set are not uniquely determined by the joint law.

Where the marginal density $f_X(x)$ is positive, a joint probability density determines the conditional densityIt integrates to one in $y$, and supplies conditional expectations by integrating against it. Its values on a probability-zero set of conditioning points are not fixed by the joint probability distribution.

A full conditional distribution is the conditional law of one component of a joint model given all the other components and the observed data. A Gibbs sampler updates components by drawing from these laws.