AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence

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A Nearly-Black-Box Online Algorithm for Joint Parameter and State Estimation in Temporal Models
Yusuf Bugra Erol, Yi Wu, Lei Li, Stuart Russell

Last modified: 2017-02-13

Abstract


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.

Keywords


state space model; joint parameter and state estimate; probabilistic programming; assumed parameter filter

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