Building Robust Simulation-based Filters for Evolving Data Sets

James Carpenter, Peter Clifford and Paul Fearnhead

Department of Statistics, 1, South Parks Road, Oxford, OX1 3TG.
Tel: +44 1865 272868 Tel: +44 1865 279737
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The need for accurate monitoring and analysis of sequential data arises in many scientific, industrial and financial problems. Although the Kalman filter is effective in the linear-Gaussian case, new methods of dealing with sequential data are required with non-standard models. Recently, there has been renewed interest in simulation-based techniques. The basic idea behind these techniques is that the current state of knowledge is encapsulated in a representative sample from the appropriate posterior distribution. As time goes on, the sample evolves and adapts recursively in accordance with newly acquired data. We give a critical review of recent developments, by reference to oil well monitoring, ion channel monitoring and tracking problems, and propose some alternative algorithms that avoid the weaknesses of the current methods.


Markov Chain Monte Carlo, Importance resampling, Recursive filters, Condensation algorithm, SIR filter, MHIR filter.

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