Fredrik Lindsten
Division of Systems and Control
Department of Information Technology
Uppsala University
Sequential Monte Carlo (SMC, a.k.a. Particle Filters) and Markov Chain Monte Carlo (MCMC) are two of the main workhorses for Bayesian inference in general probabilistic models. In this talk I will first introduce these methods in the context of dynamical systems. I will then present a principled way of combining SMC and MCMC into so-called Particle MCMC (PMCMC) methods, enabling joint state and parameter inference in general non-linear/non-Gaussian state space models. In particular, I will present a specific PMCMC method---Particle Gibbs with Ancestor Sampling---that enables computationally efficient state and parameter inferences in general state space models.