An Invitation to Sequential Monte Carlo Samplers
Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing...
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| Veröffentlicht in: | Journal of the American Statistical Association 2022-09, Vol.117 (539), p.1587-1600 |
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| container_title | Journal of the American Statistical Association |
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| creator | Dai, Chenguang Heng, Jeremy Jacob, Pierre E. Whiteley, Nick |
| description | Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits.
Supplementary materials
for this article are available online. |
| doi_str_mv | 10.1080/01621459.2022.2087659 |
| format | Article |
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Supplementary materials
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Supplementary materials
for this article are available online.</description><subject>Algorithms</subject><subject>Importance sampling</subject><subject>Interacting particle systems</subject><subject>Markov analysis</subject><subject>Markov chains</subject><subject>Monte Carlo methods</subject><subject>Monte Carlo simulation</subject><subject>Normalizing (statistics)</subject><subject>Normalizing constant</subject><subject>Parallel processing</subject><subject>Regression analysis</subject><subject>Samplers</subject><subject>Sampling</subject><subject>Sampling methods</subject><subject>Sequential inference</subject><subject>State space models</subject><subject>Statistical inference</subject><subject>Statistical methods</subject><subject>Statistics</subject><issn>0162-1459</issn><issn>1537-274X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKs_QVjwvHXysZvszVL8KFQ8qOAtTLMJbNkmNUmV_nt3qV6dwwwMz_vO8BJyTWFGQcEt0JpRUTUzBowNTcm6ak7IhFZclkyKj1MyGZlyhM7JRUobGEoqNSEw98XSf3UZcxd8kUPxaj_31ucO--I5-GyLBcZ-WON219uYLsmZwz7Zq985Je8P92-Lp3L18rhczFelEVTlklJjJCIYpxCZYjVt7ZoZcJYjVsKAEMoY4NhYaJtKCC6MExKMbIVc84ZPyc3RdxfD8FDKehP20Q8nNZOUU17XXAxUdaRMDClF6_QudluMB01Bj-Hov3D0GI7-DWfQ3R11nXchbvE7xL7VGQ99iC6iN13S_H-LHyY3ahA</recordid><startdate>20220914</startdate><enddate>20220914</enddate><creator>Dai, Chenguang</creator><creator>Heng, Jeremy</creator><creator>Jacob, Pierre E.</creator><creator>Whiteley, Nick</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope><scope>K9.</scope><orcidid>https://orcid.org/0000-0002-3126-6966</orcidid><orcidid>https://orcid.org/0000-0002-8337-626X</orcidid><orcidid>https://orcid.org/0000-0003-4959-6856</orcidid></search><sort><creationdate>20220914</creationdate><title>An Invitation to Sequential Monte Carlo Samplers</title><author>Dai, Chenguang ; Heng, Jeremy ; Jacob, Pierre E. ; Whiteley, Nick</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-11cc7aa0cf8aa28261deb2c0fe3aa54c0448cc03a9e0d954434cf470c7d47b393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Importance sampling</topic><topic>Interacting particle systems</topic><topic>Markov analysis</topic><topic>Markov chains</topic><topic>Monte Carlo methods</topic><topic>Monte Carlo simulation</topic><topic>Normalizing (statistics)</topic><topic>Normalizing constant</topic><topic>Parallel processing</topic><topic>Regression analysis</topic><topic>Samplers</topic><topic>Sampling</topic><topic>Sampling methods</topic><topic>Sequential inference</topic><topic>State space models</topic><topic>Statistical inference</topic><topic>Statistical methods</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dai, Chenguang</creatorcontrib><creatorcontrib>Heng, Jeremy</creatorcontrib><creatorcontrib>Jacob, Pierre E.</creatorcontrib><creatorcontrib>Whiteley, Nick</creatorcontrib><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><jtitle>Journal of the American Statistical Association</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dai, Chenguang</au><au>Heng, Jeremy</au><au>Jacob, Pierre E.</au><au>Whiteley, Nick</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Invitation to Sequential Monte Carlo Samplers</atitle><jtitle>Journal of the American Statistical Association</jtitle><date>2022-09-14</date><risdate>2022</risdate><volume>117</volume><issue>539</issue><spage>1587</spage><epage>1600</epage><pages>1587-1600</pages><issn>0162-1459</issn><eissn>1537-274X</eissn><abstract>Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits.
Supplementary materials
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| ispartof | Journal of the American Statistical Association, 2022-09, Vol.117 (539), p.1587-1600 |
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| subjects | Algorithms Importance sampling Interacting particle systems Markov analysis Markov chains Monte Carlo methods Monte Carlo simulation Normalizing (statistics) Normalizing constant Parallel processing Regression analysis Samplers Sampling Sampling methods Sequential inference State space models Statistical inference Statistical methods Statistics |
| title | An Invitation to Sequential Monte Carlo Samplers |
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