Bayesian Analysis of Markov Chains

author: Persi Diaconis, Stanford University
published: Jan. 19, 2010,   recorded: December 2009,   views: 10399


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Suppose we observe data and want to test if it comes from a Markov chain. If it does, we may want to estimate the transition operator. Working in a Bayesian way, we have to specify priors and compute posteriors. Interesting things happen if we want to put priors on reversible Markov chains. There are useful connections with reinforced random walk (work with Silke Rolles). On large-scale application to protein folding will be described. More generally, these problems arise in approximating a dynamical system by a Markov chain. For continuous state spaces, the usual conjugate prior analysis breaks down. Thesis work of Wai Liu (Stanford) gives useful families of priors where computations are "easy." These seem to work well in test problems and can be proved consistent.

See Also:

Download slides icon Download slides: nips09_diaconis_bamc_01.pdf (1.6┬áMB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: