On embedded hidden Markov models and particle Markov chain Monte Carlo methods

Understand

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.