On embedded hidden Markov models and particle Markov chain Monte Carlo methods (1610.08962v1)

Abstract: The embedded hidden Markov model (EHMM) sampling method is a Markov chain Monte Carlo (MCMC) technique for state inference in non-linear non-Gaussian state-space models which was proposed in Neal (2003); Neal et al. (2004) and extended in Shestopaloff and Neal (2016). An extension to Bayesian parameter inference was presented in Shestopaloff and Neal (2013). An alternative class of MCMC schemes addressing similar inference problems is provided by particle MCMC (PMCMC) methods (Andrieu et al. 2009; 2010). All these methods rely on the introduction of artificial extended target distributions for multiple state sequences which, by construction, are such that one randomly indexed sequence is distributed according to the posterior of interest. By adapting the Metropolis-Hastings algorithms developed in the framework of PMCMC methods to the EHMM framework, we obtain novel particle filter (PF)-type algorithms for state inference and novel MCMC schemes for parameter and state inference. In addition, we show that most of these algorithms can be viewed as particular cases of a general PF and PMCMC framework. We compare the empirical performance of the various algorithms on low- to high-dimensional state-space models. We demonstrate that a properly tuned conditional PF with "local" MCMC moves proposed in Shestopaloff and Neal (2016) can outperform the standard conditional PF significantly when applied to high-dimensional state-space models while the novel PF-type algorithm could prove to be an interesting alternative to standard PFs for likelihood estimation in some lower-dimensional scenarios.