Optimal estimation and Cramer-Rao bounds for partial non-gaussian state space models
Partial non-Gaussian state-space models include many models of interest while keeping a convenient analytical structure. In this paper, two problems related to partial non-Gaussian models are addressed. First, we present an efficient sequential Monte Carlo method to perform Bayesian inference. Secon...
| প্রধান লেখক: | , , |
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| বিন্যাস: | Conference item |
| প্রকাশিত: |
2001
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| _version_ | 1826294184385970176 |
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| author | Bergman, N Doucet, A Gordon, N |
| author_facet | Bergman, N Doucet, A Gordon, N |
| author_sort | Bergman, N |
| collection | OXFORD |
| description | Partial non-Gaussian state-space models include many models of interest while keeping a convenient analytical structure. In this paper, two problems related to partial non-Gaussian models are addressed. First, we present an efficient sequential Monte Carlo method to perform Bayesian inference. Second, we derive simple recursions to compute posterior Cramér-Rao bounds (PCRB). An application to jump Markov linear systems (JMLS) is given. |
| first_indexed | 2024-03-07T03:41:43Z |
| format | Conference item |
| id | oxford-uuid:be1fd07f-e50b-4e29-86f4-3ea8d986f43c |
| institution | University of Oxford |
| last_indexed | 2024-03-07T03:41:43Z |
| publishDate | 2001 |
| record_format | dspace |
| spelling | oxford-uuid:be1fd07f-e50b-4e29-86f4-3ea8d986f43c2022-03-27T05:36:54ZOptimal estimation and Cramer-Rao bounds for partial non-gaussian state space modelsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:be1fd07f-e50b-4e29-86f4-3ea8d986f43cSymplectic Elements at Oxford2001Bergman, NDoucet, AGordon, NPartial non-Gaussian state-space models include many models of interest while keeping a convenient analytical structure. In this paper, two problems related to partial non-Gaussian models are addressed. First, we present an efficient sequential Monte Carlo method to perform Bayesian inference. Second, we derive simple recursions to compute posterior Cramér-Rao bounds (PCRB). An application to jump Markov linear systems (JMLS) is given. |
| spellingShingle | Bergman, N Doucet, A Gordon, N Optimal estimation and Cramer-Rao bounds for partial non-gaussian state space models |
| title | Optimal estimation and Cramer-Rao bounds for partial non-gaussian state space models |
| title_full | Optimal estimation and Cramer-Rao bounds for partial non-gaussian state space models |
| title_fullStr | Optimal estimation and Cramer-Rao bounds for partial non-gaussian state space models |
| title_full_unstemmed | Optimal estimation and Cramer-Rao bounds for partial non-gaussian state space models |
| title_short | Optimal estimation and Cramer-Rao bounds for partial non-gaussian state space models |
| title_sort | optimal estimation and cramer rao bounds for partial non gaussian state space models |
| work_keys_str_mv | AT bergmann optimalestimationandcramerraoboundsforpartialnongaussianstatespacemodels AT douceta optimalestimationandcramerraoboundsforpartialnongaussianstatespacemodels AT gordonn optimalestimationandcramerraoboundsforpartialnongaussianstatespacemodels |