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Monte Carlo Filtering of Piecewise Deterministic Processes

by Nick Whiteley, Adam Johansen and Simon Godsill

We present efficient Monte Carlo algorithms for performing Bayesian inference in a broad class of models: those in which the distributions of interest may be represented by time marginals of continuous-time jump processes conditional on a realisation of some noisy observation sequence. The sequential nature of the proposed algorithm makes it particularly suitable for online estimation in time series. We demonstrate that two existing schemes can be interpreted as particular cases of the proposed method. Results are provided which illustrate significant performance improvements relative to existing methods.

Key words: Optimal Filtering, Sequential Monte Carlo, Particle Filtering

Full text of the paper (pdf), to appear in the Journal of Computational and Graphical Statistics

Fionnuala Hill, SuSTaIn Coordinator, School of Mathematics, University of Bristol, Bristol, BS8 1TW, UK.
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