MCMC for non-linear state space models using ensembles of latent sequences

Understand

Non-linear state space models are a widely-used class of models for biological, economic, and physical processes.

• Fitting these models to observed data is a difficult inference problem that has no straightforward solution.
• We take a Bayesian approach to the inference of unknown parameters of a non-linear state model; this, in turn, requires the availability of efficient Markov Chain Monte Carlo (MCMC) sampling methods for the latent (hidden) variables and model parameters.
• Using the ensemble technique of Neal (2010) and the embedded HMM technique of Neal (2003), we introduce a new Markov Chain Monte Carlo method for non-linear state space models.