By Topic

Estimation via Markov chain Monte Carlo

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Spall, J.C. ; Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA

Markov chain Monte Carlo (MCMC) is a powerful means for generating random samples that can be used in computing statistical estimates, numerical integrals, and marginal and joint probabilities. The approach is especially useful in applications where one is forming an estimate based on a multivariate probability distribution or density function that would be hopeless to obtain analytically. In particular, MCMC provides a means for generating samples from joint distributions based on easier sampling from conditional distributions. Over the last 10 to 15 years, the approach has had a large impact on the theory and practice of statistical modeling. On the other hand, MCMC has had relatively little impact (yet) on estimation problems in control. The paper is a survey of popular implementations of MCMC, focusing especially on the two most popular specific implementations of MCMC: Metropolis-Hastings and Gibbs sampling.

Published in:

American Control Conference, 2002. Proceedings of the 2002  (Volume:4 )

Date of Conference:

2002