1
$\begingroup$

Given a Bayesian network and evidence for the values of a subset of the variables, a standard question is to compute the posterior distribution on the remaining variables. The Gibbs sampling technique gives a discrete time Markov chain which samples from this posterior distribution. Is there a continuous time Markov process which does the same thing?

$\endgroup$
1
$\begingroup$

For any discrete time Markov chain, you can get a continuous time Markov chain with the same stationary measure: just perform the same transitions as the discrete time Markov chain, but at times given by a Poisson process.

$\endgroup$

Your Answer

By clicking "Post Your Answer", you acknowledge that you have read our updated terms of service, privacy policy and cookie policy, and that your continued use of the website is subject to these policies.

Not the answer you're looking for? Browse other questions tagged or ask your own question.