Application of Girsanov Theorem to Particle Filtering of Discretely Observed Continuous-Time Non-Linear Systems

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dc.creatorSärkkä, Simo
dc.creatorSottinen, Tommi
dc.date2007-05-11
dc.date2008-04-29
dc.date.accessioned2026-07-07T09:35:22Z
dc.date.available2026-07-07T09:35:22Z
dc.descriptionThis article considers the application of particle filtering to continuous-discrete optimal filtering problems, where the system model is a stochastic differential equation, and noisy measurements of the system are obtained at discrete instances of time. It is shown how the Girsanov theorem can be used for evaluating the likelihood ratios needed in importance sampling. It is also shown how the methodology can be applied to a class of models, where the driving noise process is lower in the dimensionality than the state and thus the laws of state and noise are not absolutely continuous. Rao-Blackwellization of conditionally Gaussian models and unknown static parameter models is also considered.
dc.descriptionPaper in form as it has been accepted to Bayesian Analysis
dc.identifierhttps://arxiv.org/abs/0705.1598
dc.identifierhttp://arxiv.org/abs/0705.1598
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/159811
dc.subjectMethodology
dc.subjectComputation
dc.titleApplication of Girsanov Theorem to Particle Filtering of Discretely Observed Continuous-Time Non-Linear Systems
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