Bayesian analysis for reversible Markov chains
By Persi Diaconis, Silke W. W. Rolles. 2006
81 citations recorded by the source corpus.
We introduce a natural conjugate prior for the transition matrix of a reversible Markov chain. This allows estimation and testing. The prior arises from random walk with reinforcement in the same way the Dirichlet prior arises from Polya’s urn. We give closed form normalizing constants, a simple method of simulation from the posterior and a characterization along the lines of W. E. Johnson’s characterization of the Dirichlet prior.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/040107b5f4b17aadbb216ba355cdf4a4c3a0b696
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In Artificial Intelligence
In Statistics and Probability
Citations in the corpus
- A survey of random processes with reinforcement (2006, background): “ law of this sequence is a mixture of laws of Markov chains. Given a set of initial weights one the edges, the mixing measure may be explicitly described, as in Theorem 5.1 below. Diaconis and Rolles [DR06] propose this family of such measures, with initial weights as parameters, as priors over reve”