  In this paper we consider fully Bayesian inference in general state space
models. Existing particle Markov chain Monte Carlo (MCMC) algorithms use an
augmented model that takes into account all the variable sampled in a
sequential Monte Carlo algorithm. This paper describes an approach that also
uses sequential Monte Carlo to construct an approximation to the state space,
but generates extra states using MCMC runs at each time point. We construct an
augmented model for our extended space with the marginal distribution of the
sampled states matching the posterior distribution of the state vector. We show
how our method may be combined with particle independent Metropolis-Hastings or
particle Gibbs steps to obtain a smoothing algorithm. All the Metropolis
acceptance probabilities are identical to those obtained in existing
approaches, so there is no extra cost in term of Metropolis-Hastings rejections
when using our approach. The number of MCMC iterates at each time point is
chosen by the used and our augmented model collapses back to the model in
Olsson and Ryden (2011) when the number of MCMC iterations reduces. We show
empirically that our approach works well on applied examples and can outperform
existing methods.
