# AP Statistics Curriculum 2007 Bayesian Gibbs

### From Socr

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==[[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, also making it an example of a Markov chain Monte Carlo algorithm. |

==Introduction to numerical methods== | ==Introduction to numerical methods== |

## Revision as of 17:39, 28 June 2010

## 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.

## Introduction to numerical methods

## EM algorithm

## Data augmentation by Monte Carlo

## The Gibbs Sampler

## Rejection Sampling

## Metropolis Hastings Algorithm

## Generalized Linear Model

## See also

## References

- Expectation Maximization and Mixture Modeling Tutorial (December 9, 2008). Statistics Online Computational Resource. Paper EM_MM, http://repositories.cdlib.org/socr/EM_MM.

- SOCR Home page: http://www.socr.ucla.edu

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