Parameter Estimation for Hidden Markov models with Intractable Likelihoods

In this talk I consider sequential Monte Carlo (SMC) methods for hidden Markov models. In the scenario for which the conditional density of the observations given the latent state is intractable we give a simple ABC approximation of the model along with some basic SMC algorithms for sampling from the associated filtering distribution. Then, we consider the problem of smoothing, given access to a batch data set. We present a simulation technique which combines forward only smoothing (Del Moral et al, 2011) and particle Markov chain Monte Carlo (Andrieu et al 2010), for an algorithm which scales linearly in the number of particles