Practical Filtering with Sequential Parameter Learning
Nicholas G. Polson, Jonathan R. Stroud, Peter Müller
University of Chicago, University of Pennsylvania, and MD Anderson Cancer Center
This paper develops a simulation-based approach to sequential parameter learning and filtering
in general state-space models. Our approach is based on approximating the target posterior
by a mixture of fixed-lag smoothing distributions. Parameter inference exploits a sufficient
statistic structure and the methodology can be easily implemented by modifying state space
smoothing algorithms. We avoid reweighting particles and hence sample degeneracy problems
that plague particle filters that use sequential importance sampling. The method is illustrated
using two examples: a benchmark autoregressive model with observation error and a high-dimensional
dynamic spatio-temporal model. We show that the method provides accurate inference in the presence
of outliers, model misspecification and high dimensionality.
Keywords: Filtering, Markov Chain Monte Carlo, Particle Filtering, Sequential Parameter Learning,
Spatio-Temporal Models, State-Space Models.
The manuscript is available in PDF format.