Sergio Bacallado (Stanford University)
23 September 2011 @ 12:00
- Past event
A Bayesian analysis of reversible time series with an uncertain length of memory
We propose a Bayesian analysis of reversible time series using a Probabilistic Suffix Automaton (PSA) model. We show that PSAs have a representation as higher-order Markov chains, and that the class of reversible PSAs generalize reversible variable-order Markov chains. The analysis uses a conjugate prior for higher-order Markov chains (Bacallado, Annals of Statistics, 39 (2), 2011), which allows us to sample the posterior of the process and latent lengths of memory through a blocked Gibbs sampler. We show the application of the method to a dataset of molecular dynamics simulations of protein folding.