Uniform time average consistency of Monte Carlo particle filters
By Ramon van Handel. 2008
52 citations recorded by the source corpus.
We prove that bootstrap-type Monte Carlo particle filters approximate the optimal nonlinear filter in a time average sense uniformly with respect to the time horizon when the signal is ergodic and the particle system satisfies a tightness property. The latter is satisfied without further assumptions when the signal state space is compact, as well as in the noncompact setting when the signal is geometrically ergodic and the observations satisfy additional regularity assumptions.
Subfields: Artificial Intelligence, Demography, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/31c94e4472b79c4119a71c1e54c4ef6e8988b327
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In Artificial Intelligence
- D. Crisan
- Alexandros Beskos
- Ajay Jasra
- Nick Whiteley
- Gareth O. Roberts
- Omiros Papaspiliopoulos
- Paul Fearnhead
- Chenxu Li
In Demography
In Statistical and Nonlinear Physics
Citations in the corpus
- Error Bounds and Normalising Constants for Sequential Monte Carlo Samplers in High Dimensions (2014, background): “[29]), perhaps considerable research is needed with regards to dimension of the deterministic (true) filter, before a full analysis of numerical algorithms can be undertaken.”