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We present a scalable approach to performing approximate fully Bayesianinference in generic state space models. The proposed method is an alternativeto particle MCMC that provides fully Bayesian inference of both the dynamiclatent states and the static parameters of the model. We build up on recentadvances in computational statistics that combine variational methods withsequential Monte Carlo sampling and we demonstrate the advantages of performingfull Bayesian inference over the static parameters rather than just performingvariational EM approximations. We illustrate how our approach enables scalableinference in multivariate stochastic volatility models and self-exciting pointprocess models that allow for flexible dynamics in the latent intensityfunction.

Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers