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• W1837109035 abstract "This 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 the state and the noise are not absolutely continuous. Rao-Blackwellization of conditionally Gaussian models and unknown static parameter models is also considered." @default.
• W1837109035 created "2016-06-24" @default.
• W1837109035 creator A5027613699 @default.
• W1837109035 creator A5075994876 @default.
• W1837109035 date "2008-09-01" @default.
• W1837109035 modified "2023-09-23" @default.
• W1837109035 title "Application of Girsanov theorem to particle filtering of discretely observed continuous-time non-linear systems" @default.
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• W1837109035 doi "https://doi.org/10.1214/08-ba322" @default.
• W1837109035 hasPublicationYear "2008" @default.
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