Journal
BAYESIAN ANALYSIS
Volume 3, Issue 3, Pages 555-584Publisher
INT SOC BAYESIAN ANALYSIS
DOI: 10.1214/08-BA322
Keywords
Girsanov theorem; particle filtering; continuous-discrete filtering
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available