# AP Statistics Curriculum 2007 Bayesian Gibbs

(Difference between revisions)
 Revision as of 06:02, 2 June 2009 (view source)JayZzz (Talk | contribs) (New page: Gibbs sampling is an algorithm to generate a sequence of samples from the joint probability distribution of two or more random variables. The purpose of this sequence is to approximate the...)← Older edit Current revision as of 21:11, 28 June 2010 (view source)Jenny (Talk | contribs) (2 intermediate revisions not shown) Line 1: Line 1: - Gibbs sampling is an algorithm to generate a sequence of samples from the joint probability distribution of two or more random variables. The purpose of this sequence is to approximate the joint distribution, or to compute an expected value. Gibbs sampling is a special case of the Metropolis-Hastings algorithm also making it an example of a Markov chain Monte Carlo algorithm. + ==[[EBook | Probability and Statistics Ebook]] - Expectation Maximization Estimation, Gibbs Sampling and Monte Carlo Simulations== + Gibbs sampling is an algorithm to generate a sequence of samples from the joint probability distribution of two or more random variables. The purpose of this sequence is to approximate the joint distribution, or to compute an expected value. Gibbs sampling is a special case of the Metropolis-Hastings algorithm and is also  an example of a Markov chain Monte Carlo algorithm. ==Introduction to numerical methods== ==Introduction to numerical methods== - ==EM algorithm== ==EM algorithm== - ==Data augmentation by Monte Carlo== ==Data augmentation by Monte Carlo== - Line 23: Line 21: ==Metropolis Hastings Algorithm== ==Metropolis Hastings Algorithm== - ==Generalized Linear Model== ==Generalized Linear Model== + + + ==See also== + * [[EBook#Chapter_III:_Probability |Probability Chapter]] + + ==References== + * [http://repositories.cdlib.org/socr/EM_MM Expectation Maximization and Mixture Modeling Tutorial] (December 9, 2008). Statistics Online Computational Resource. Paper EM_MM, http://repositories.cdlib.org/socr/EM_MM. + +

## Probability and Statistics Ebook - Expectation Maximization Estimation, Gibbs Sampling and Monte Carlo Simulations

Gibbs sampling is an algorithm to generate a sequence of samples from the joint probability distribution of two or more random variables. The purpose of this sequence is to approximate the joint distribution, or to compute an expected value. Gibbs sampling is a special case of the Metropolis-Hastings algorithm and is also an example of a Markov chain Monte Carlo algorithm.