Scalable Bayesian Inference for Coupled Hidden Markov and Semi-Markov Models.

ABSTRACT: Bayesian inference for coupled hidden Markov models frequently relies on data augmentation techniques for imputation of the hidden state processes. Considerable progress has been made on developing such techniques, mainly using Markov chain Monte Carlo (MCMC) methods. However, as the dimensionality and complexity of the hidden processes increase some of these methods become inefficient, either because they produce MCMC chains with high autocorrelation or because they become computationally intractable. Motivated by this fact we developed a novel MCMC algorithm, which is a modification of the forward filtering backward sampling algorithm, that achieves a good balance between computation and mixing properties, and thus can be used to analyze models with large numbers of hidden chains. Even t

SUBMITTER: Touloupou P 

PROVIDER: S-EPMC7455056 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature