Difference between revisions of "Gibbs sampling"
From Cohen Courses
Jump to navigationJump to search| Line 1: | Line 1: | ||
Gibbs sampling is used to sample from the stable joint distribution of two or more random variables when accurate computation of the integral or a marginal is intractable. Usually some variables in this set of random variables are the actual observables and hence there values need not be sampled in the [[UsesMethod :: Gibbs sampling]] iterations. This form of approximate inference method is generally used when doing posterior probability inference in probabilistic graphical models where computation of marginals are intractable. | Gibbs sampling is used to sample from the stable joint distribution of two or more random variables when accurate computation of the integral or a marginal is intractable. Usually some variables in this set of random variables are the actual observables and hence there values need not be sampled in the [[UsesMethod :: Gibbs sampling]] iterations. This form of approximate inference method is generally used when doing posterior probability inference in probabilistic graphical models where computation of marginals are intractable. | ||
| + | |||
| + | |||
| + | :<math>p(x_j|x_1,\dots,x_{j-1},x_{j+1},\dots,x_n) = \frac{p(x_1,\dots,x_n)}{p(x_1,\dots,x_{j-1},x_{j+1},\dots,x_n)} \propto p(x_1,\dots,x_n)</math> | ||
Revision as of 00:20, 1 April 2011
Gibbs sampling is used to sample from the stable joint distribution of two or more random variables when accurate computation of the integral or a marginal is intractable. Usually some variables in this set of random variables are the actual observables and hence there values need not be sampled in the Gibbs sampling iterations. This form of approximate inference method is generally used when doing posterior probability inference in probabilistic graphical models where computation of marginals are intractable.
