SMC samplers for Bayesian optimal nonlinear design
Experimental design is a fundamental problem in science. It arises in the planning of medical trials, sensor network deployment and control as well as in costly data gathering in physics, chemistry and biology. Bayesian decision theory provides a principled way of treating this problem, but leads to...
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| Format: | Conference item |
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2006
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| _version_ | 1797061316708401152 |
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| author | Kueck, H de Freitas, N Doucet, A IEEE |
| author_facet | Kueck, H de Freitas, N Doucet, A IEEE |
| author_sort | Kueck, H |
| collection | OXFORD |
| description | Experimental design is a fundamental problem in science. It arises in the planning of medical trials, sensor network deployment and control as well as in costly data gathering in physics, chemistry and biology. Bayesian decision theory provides a principled way of treating this problem, but leads to an intractable joint optimization and integration problem. Here, we propose a viable solution to this hard computational problem using sequential Monte Carlo samplers. © 2006 IEEE. |
| first_indexed | 2024-03-06T20:29:21Z |
| format | Conference item |
| id | oxford-uuid:3088fcb0-2054-4970-950d-374d270ac31b |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:29:21Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | oxford-uuid:3088fcb0-2054-4970-950d-374d270ac31b2022-03-26T13:01:54ZSMC samplers for Bayesian optimal nonlinear designConference itemhttp://purl.org/coar/resource_type/c_5794uuid:3088fcb0-2054-4970-950d-374d270ac31bSymplectic Elements at Oxford2006Kueck, Hde Freitas, NDoucet, AIEEEExperimental design is a fundamental problem in science. It arises in the planning of medical trials, sensor network deployment and control as well as in costly data gathering in physics, chemistry and biology. Bayesian decision theory provides a principled way of treating this problem, but leads to an intractable joint optimization and integration problem. Here, we propose a viable solution to this hard computational problem using sequential Monte Carlo samplers. © 2006 IEEE. |
| spellingShingle | Kueck, H de Freitas, N Doucet, A IEEE SMC samplers for Bayesian optimal nonlinear design |
| title | SMC samplers for Bayesian optimal nonlinear design |
| title_full | SMC samplers for Bayesian optimal nonlinear design |
| title_fullStr | SMC samplers for Bayesian optimal nonlinear design |
| title_full_unstemmed | SMC samplers for Bayesian optimal nonlinear design |
| title_short | SMC samplers for Bayesian optimal nonlinear design |
| title_sort | smc samplers for bayesian optimal nonlinear design |
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