Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection
Summary Stochastic gradient Markov chain Monte Carlo algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with...
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| Veröffentlicht in: | Biometrika 2020-12, Vol.107 (4), p.997-1004 |
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| container_issue | 4 |
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| container_title | Biometrika |
| container_volume | 107 |
| creator | Song, Qifan Sun, Yan Ye, Mao Liang, Faming |
| description | Summary
Stochastic gradient Markov chain Monte Carlo algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters. This paper proposes an extended stochastic gradient Markov chain Monte Carlo algorithm which, by introducing appropriate latent variables, can be applied to more general large-scale Bayesian computing problems, such as those involving dimension jumping and missing data. Numerical studies show that the proposed algorithm is highly scalable and much more efficient than traditional Markov chain Monte Carlo algorithms. |
| doi_str_mv | 10.1093/biomet/asaa029 |
| format | Article |
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Stochastic gradient Markov chain Monte Carlo algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters. This paper proposes an extended stochastic gradient Markov chain Monte Carlo algorithm which, by introducing appropriate latent variables, can be applied to more general large-scale Bayesian computing problems, such as those involving dimension jumping and missing data. Numerical studies show that the proposed algorithm is highly scalable and much more efficient than traditional Markov chain Monte Carlo algorithms.</description><identifier>ISSN: 0006-3444</identifier><identifier>EISSN: 1464-3510</identifier><identifier>DOI: 10.1093/biomet/asaa029</identifier><language>eng</language><publisher>Oxford: Oxford University Press</publisher><subject>Algorithms ; Bayesian analysis ; Computation ; Markov analysis ; Markov chains ; Missing data ; Parameters ; Railroad transportation</subject><ispartof>Biometrika, 2020-12, Vol.107 (4), p.997-1004</ispartof><rights>2020 Biometrika Trust 2020</rights><rights>2020 Biometrika Trust</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c271t-562fd41e3fb7f0a62ae78d3925b9450f27ab104db6225e0ef51fec9e3ce42f153</citedby><cites>FETCH-LOGICAL-c271t-562fd41e3fb7f0a62ae78d3925b9450f27ab104db6225e0ef51fec9e3ce42f153</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Song, Qifan</creatorcontrib><creatorcontrib>Sun, Yan</creatorcontrib><creatorcontrib>Ye, Mao</creatorcontrib><creatorcontrib>Liang, Faming</creatorcontrib><title>Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection</title><title>Biometrika</title><description>Summary
Stochastic gradient Markov chain Monte Carlo algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters. This paper proposes an extended stochastic gradient Markov chain Monte Carlo algorithm which, by introducing appropriate latent variables, can be applied to more general large-scale Bayesian computing problems, such as those involving dimension jumping and missing data. Numerical studies show that the proposed algorithm is highly scalable and much more efficient than traditional Markov chain Monte Carlo algorithms.</description><subject>Algorithms</subject><subject>Bayesian analysis</subject><subject>Computation</subject><subject>Markov analysis</subject><subject>Markov chains</subject><subject>Missing data</subject><subject>Parameters</subject><subject>Railroad transportation</subject><issn>0006-3444</issn><issn>1464-3510</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNqFkD1PwzAQhi0EEqWwMltiYkhrO7ZDRqjKh9SKBebo4pyLSxoX263ovydVujOd7tXz3kkPIbecTTgr82nt_AbTFCIAE-UZGXGpZZYrzs7JiDGms1xKeUmuYlwfV630iMD8N2HXYENj8uYLYnKGrgI0DrtElxC-_Z72uevo0ncJ6QxC66n1gbYQVphFAy3SJzhgdNDRPQQHdZ9EbNEk57trcmGhjXhzmmPy-Tz_mL1mi_eXt9njIjOi4ClTWthGcsxtXVgGWgAWD01eClWXUjErCqg5k02thVDI0Cpu0ZSYG5TCcpWPyd1wdxv8zw5jqtZ-F7r-ZSVkobkSTLOemgyUCT7GgLbaBreBcKg4q44aq0FjddLYF-6Hgt9t_2P_ANjhd3Q</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Song, Qifan</creator><creator>Sun, Yan</creator><creator>Ye, Mao</creator><creator>Liang, Faming</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20201201</creationdate><title>Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection</title><author>Song, Qifan ; Sun, Yan ; Ye, Mao ; Liang, Faming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c271t-562fd41e3fb7f0a62ae78d3925b9450f27ab104db6225e0ef51fec9e3ce42f153</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Bayesian analysis</topic><topic>Computation</topic><topic>Markov analysis</topic><topic>Markov chains</topic><topic>Missing data</topic><topic>Parameters</topic><topic>Railroad transportation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Song, Qifan</creatorcontrib><creatorcontrib>Sun, Yan</creatorcontrib><creatorcontrib>Ye, Mao</creatorcontrib><creatorcontrib>Liang, Faming</creatorcontrib><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Biometrika</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Song, Qifan</au><au>Sun, Yan</au><au>Ye, Mao</au><au>Liang, Faming</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection</atitle><jtitle>Biometrika</jtitle><date>2020-12-01</date><risdate>2020</risdate><volume>107</volume><issue>4</issue><spage>997</spage><epage>1004</epage><pages>997-1004</pages><issn>0006-3444</issn><eissn>1464-3510</eissn><abstract>Summary
Stochastic gradient Markov chain Monte Carlo algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters. This paper proposes an extended stochastic gradient Markov chain Monte Carlo algorithm which, by introducing appropriate latent variables, can be applied to more general large-scale Bayesian computing problems, such as those involving dimension jumping and missing data. Numerical studies show that the proposed algorithm is highly scalable and much more efficient than traditional Markov chain Monte Carlo algorithms.</abstract><cop>Oxford</cop><pub>Oxford University Press</pub><doi>10.1093/biomet/asaa029</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0006-3444 |
| ispartof | Biometrika, 2020-12, Vol.107 (4), p.997-1004 |
| issn | 0006-3444 1464-3510 |
| language | eng |
| recordid | cdi_proquest_journals_2476152060 |
| source | Oxford University Press Journals All Titles (1996-Current) |
| subjects | Algorithms Bayesian analysis Computation Markov analysis Markov chains Missing data Parameters Railroad transportation |
| title | Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection |
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