Sequential Monte Carlo samplers
We propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated ove...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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2006
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| _version_ | 1797066602176315392 |
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| author | Del Moral, P Doucet, A Jasra, A |
| author_facet | Del Moral, P Doucet, A Jasra, A |
| author_sort | Del Moral, P |
| collection | OXFORD |
| description | We propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time by using sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make parallel Markov chain Monte Carlo algorithms interact to perform global optimization and sequential Bayesian estimation and to compute ratios of normalizing constants. We illustrate these algorithms for various integration tasks arising in the context of Bayesian inference. © 2006 Royal Statistical Society. |
| first_indexed | 2024-03-06T21:44:26Z |
| format | Journal article |
| id | oxford-uuid:49121789-b2e8-43da-a6ff-5ec39a6f1b33 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:44:26Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | oxford-uuid:49121789-b2e8-43da-a6ff-5ec39a6f1b332022-03-26T15:29:25ZSequential Monte Carlo samplersJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:49121789-b2e8-43da-a6ff-5ec39a6f1b33EnglishSymplectic Elements at Oxford2006Del Moral, PDoucet, AJasra, AWe propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time by using sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make parallel Markov chain Monte Carlo algorithms interact to perform global optimization and sequential Bayesian estimation and to compute ratios of normalizing constants. We illustrate these algorithms for various integration tasks arising in the context of Bayesian inference. © 2006 Royal Statistical Society. |
| spellingShingle | Del Moral, P Doucet, A Jasra, A Sequential Monte Carlo samplers |
| title | Sequential Monte Carlo samplers |
| title_full | Sequential Monte Carlo samplers |
| title_fullStr | Sequential Monte Carlo samplers |
| title_full_unstemmed | Sequential Monte Carlo samplers |
| title_short | Sequential Monte Carlo samplers |
| title_sort | sequential monte carlo samplers |
| work_keys_str_mv | AT delmoralp sequentialmontecarlosamplers AT douceta sequentialmontecarlosamplers AT jasraa sequentialmontecarlosamplers |