A Nearly-Black-Box Online Algorithm for Joint Parameter and State Estimation in Temporal Models

Online joint parameter and state estimation is a core problem for temporal models. Most existing methods are either restricted to a particular class of models (e.g., the Storvik filter) or computationally expensive (e.g., particle MCMC). We propose a novel nearly-black-box algorithm, the Assumed Parameter Filter (APF), a hybrid of particle filtering for state variables and assumed density filtering for parameter variables. It has the following advantages: (a) it is online and computationally efficient; (b) it is applicable to both discrete and continuous parameter spaces with arbitrary transition dynamics. On a variety of toy and real models, APF generates more accurate results within a fixed computation budget compared to several standard algorithms from the literature.