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
email: clifford@stats.ox.ac.uk or clifford@jesus.ox.ac.uk
Summary
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.
Keywords
Markov Chain Monte Carlo, Importance resampling, Recursive
filters, Condensation algorithm, SIR filter, MHIR filter.
Postscript (4 pages on 1 side) Copy of Paper
Postscript (8 pages on 1 side) Copy of Paper