We present a scalable approach to performing approximate fully Bayesian
inference in generic state space models. The proposed method is an alternative
to particle MCMC that provides fully Bayesian inference of both the dynamic
latent states and the static parameters of the model. We build up on recent
advances in computational statistics that combine variational methods with
sequential Monte Carlo sampling and we demonstrate the advantages of performing
full Bayesian inference over the static parameters rather than just performing
variational EM approximations. We illustrate how our approach enables scalable
inference in multivariate stochastic volatility models and self-exciting point
process models that allow for flexible dynamics in the latent intensity
function.Comment: To appear in AISTATS 201
