On adaptive resampling strategies for sequential Monte Carlo methods
Sequential Monte Carlo (SMC) methods are a class of techniques to sample approximately from any sequence of probability distributions using a combination of importance sampling and resampling steps. This paper is concerned with the convergence analysis of a class of SMC methods where the times at wh...
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| Format: | Journal article |
| Language: | English |
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2012
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| _version_ | 1826291429269307392 |
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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 | Sequential Monte Carlo (SMC) methods are a class of techniques to sample approximately from any sequence of probability distributions using a combination of importance sampling and resampling steps. This paper is concerned with the convergence analysis of a class of SMC methods where the times at which resampling occurs are computed online using criteria such as the effective sample size. This is a popular approach amongst practitioners but there are very few convergence results available for these methods. By combining semigroup techniques with an original coupling argument, we obtain functional central limit theorems and uniform exponential concentration estimates for these algorithms. © 2012 ISI/BS. |
| first_indexed | 2024-03-07T02:59:15Z |
| format | Journal article |
| id | oxford-uuid:b0628f82-47c7-4086-9720-03dd1488698f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:59:15Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oxford-uuid:b0628f82-47c7-4086-9720-03dd1488698f2022-03-27T03:56:02ZOn adaptive resampling strategies for sequential Monte Carlo methodsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b0628f82-47c7-4086-9720-03dd1488698fEnglishSymplectic Elements at Oxford2012Del Moral, PDoucet, AJasra, ASequential Monte Carlo (SMC) methods are a class of techniques to sample approximately from any sequence of probability distributions using a combination of importance sampling and resampling steps. This paper is concerned with the convergence analysis of a class of SMC methods where the times at which resampling occurs are computed online using criteria such as the effective sample size. This is a popular approach amongst practitioners but there are very few convergence results available for these methods. By combining semigroup techniques with an original coupling argument, we obtain functional central limit theorems and uniform exponential concentration estimates for these algorithms. © 2012 ISI/BS. |
| spellingShingle | Del Moral, P Doucet, A Jasra, A On adaptive resampling strategies for sequential Monte Carlo methods |
| title | On adaptive resampling strategies for sequential Monte Carlo methods |
| title_full | On adaptive resampling strategies for sequential Monte Carlo methods |
| title_fullStr | On adaptive resampling strategies for sequential Monte Carlo methods |
| title_full_unstemmed | On adaptive resampling strategies for sequential Monte Carlo methods |
| title_short | On adaptive resampling strategies for sequential Monte Carlo methods |
| title_sort | on adaptive resampling strategies for sequential monte carlo methods |
| work_keys_str_mv | AT delmoralp onadaptiveresamplingstrategiesforsequentialmontecarlomethods AT douceta onadaptiveresamplingstrategiesforsequentialmontecarlomethods AT jasraa onadaptiveresamplingstrategiesforsequentialmontecarlomethods |