Difference between revisions of "AP Statistics Curriculum 2007 Bayesian Gibbs"
| 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, also making it an example of a Markov chain Monte Carlo algorithm. |
==Introduction to numerical methods== | ==Introduction to numerical methods== | ||
Revision as of 12:45, 28 June 2010
Contents
- 1 Probability and Statistics Ebook - Expectation Maximization Estimation, Gibbs Sampling and Monte Carlo Simulations
- 2 Introduction to numerical methods
- 3 EM algorithm
- 4 Data augmentation by Monte Carlo
- 5 The Gibbs Sampler
- 6 Rejection Sampling
- 7 Metropolis Hastings Algorithm
- 8 Generalized Linear Model
- 9 See also
- 10 References
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
Translate this page:
