# AP Statistics Curriculum 2007 Bayesian Gibbs

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 Revision as of 23:31, 22 October 2009 (view source)IvoDinov (Talk | contribs)← Older edit Current revision as of 21:11, 28 June 2010 (view source)Jenny (Talk | contribs) (One intermediate revision not shown) Line 1: Line 1: ==[[EBook | Probability and Statistics Ebook]] - Expectation Maximization Estimation, Gibbs Sampling and Monte Carlo Simulations== ==[[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 also making it an example of a Markov chain Monte Carlo algorithm. + 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==

## 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.

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