Multidimensional Laplace formulas for nonlinear Bayesian estimation
The Laplace method and Monte Carlo methods are techniques to approximate integrals which are useful in nonlinear Bayesian computation. When the model is one-dimensional, Laplace formulas to compute posterior expectations and variances have been proposed by Tierney, Kass and Kadane (1989). We provide...
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creator | Bui Quang, P. Musso, C. Le Gland, F. |
description | The Laplace method and Monte Carlo methods are techniques to approximate integrals which are useful in nonlinear Bayesian computation. When the model is one-dimensional, Laplace formulas to compute posterior expectations and variances have been proposed by Tierney, Kass and Kadane (1989). We provide in this article formulas for the multidimensional case. We demonstrate the accuracy of these formulas and show how to use them in importance sampling to design an importance density function which reduces the Monte Carlo error. |
format | Conference Proceeding |
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When the model is one-dimensional, Laplace formulas to compute posterior expectations and variances have been proposed by Tierney, Kass and Kadane (1989). We provide in this article formulas for the multidimensional case. We demonstrate the accuracy of these formulas and show how to use them in importance sampling to design an importance density function which reduces the Monte Carlo error.</description><identifier>ISSN: 2219-5491</identifier><identifier>ISBN: 1467310689</identifier><identifier>ISBN: 9781467310680</identifier><identifier>EISSN: 2219-5491</identifier><language>eng</language><publisher>IEEE</publisher><subject>Approximation methods ; Bayesian methods ; Computational modeling ; importance sampling ; Laplace method ; Monte Carlo methods ; Nonlinear Bayesian estimation ; Numerical models ; Optimized production technology ; Probability density function</subject><ispartof>2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 2012, p.1890-1894</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6334291$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6334291$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Bui Quang, P.</creatorcontrib><creatorcontrib>Musso, C.</creatorcontrib><creatorcontrib>Le Gland, F.</creatorcontrib><title>Multidimensional Laplace formulas for nonlinear Bayesian estimation</title><title>2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)</title><addtitle>EUSIPCO</addtitle><description>The Laplace method and Monte Carlo methods are techniques to approximate integrals which are useful in nonlinear Bayesian computation. When the model is one-dimensional, Laplace formulas to compute posterior expectations and variances have been proposed by Tierney, Kass and Kadane (1989). We provide in this article formulas for the multidimensional case. We demonstrate the accuracy of these formulas and show how to use them in importance sampling to design an importance density function which reduces the Monte Carlo error.</description><subject>Approximation methods</subject><subject>Bayesian methods</subject><subject>Computational modeling</subject><subject>importance sampling</subject><subject>Laplace method</subject><subject>Monte Carlo methods</subject><subject>Nonlinear Bayesian estimation</subject><subject>Numerical models</subject><subject>Optimized production technology</subject><subject>Probability density function</subject><issn>2219-5491</issn><issn>2219-5491</issn><isbn>1467310689</isbn><isbn>9781467310680</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpNTctOwzAQtKBIlNIv4JIfiOT12k5yhIhHpSAu9Fxt47Vk5DhVnB769wTBgbnMSPO6EmuloCmNbuBa3IG2FYK0dbP6Z9yKbc5fckGtjFJyLdr3c5yDCwOnHMZEsejoFKnnwo_TcI6Uf0SRxhRDYpqKJ7pwDpQKznMYaF5K9-LGU8y8_eON2L88f7ZvZffxumsfuzJAZeYSDKoK3NEbLwldb5VTUrseXc2IaLTiRfijdZLZaKsBPC0BrBgBHeNGPPzuBmY-nKblfrocLKJWDeA3xcRH5A</recordid><startdate>201208</startdate><enddate>201208</enddate><creator>Bui Quang, P.</creator><creator>Musso, C.</creator><creator>Le Gland, F.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201208</creationdate><title>Multidimensional Laplace formulas for nonlinear Bayesian estimation</title><author>Bui Quang, P. ; Musso, C. ; Le Gland, F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-153271dbf5f0a3dc62d204dc3d8e333542e8e3fb6d0ee546411fa04d37e313de3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Approximation methods</topic><topic>Bayesian methods</topic><topic>Computational modeling</topic><topic>importance sampling</topic><topic>Laplace method</topic><topic>Monte Carlo methods</topic><topic>Nonlinear Bayesian estimation</topic><topic>Numerical models</topic><topic>Optimized production technology</topic><topic>Probability density function</topic><toplevel>online_resources</toplevel><creatorcontrib>Bui Quang, P.</creatorcontrib><creatorcontrib>Musso, C.</creatorcontrib><creatorcontrib>Le Gland, F.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bui Quang, P.</au><au>Musso, C.</au><au>Le Gland, F.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Multidimensional Laplace formulas for nonlinear Bayesian estimation</atitle><btitle>2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)</btitle><stitle>EUSIPCO</stitle><date>2012-08</date><risdate>2012</risdate><spage>1890</spage><epage>1894</epage><pages>1890-1894</pages><issn>2219-5491</issn><eissn>2219-5491</eissn><isbn>1467310689</isbn><isbn>9781467310680</isbn><abstract>The Laplace method and Monte Carlo methods are techniques to approximate integrals which are useful in nonlinear Bayesian computation. When the model is one-dimensional, Laplace formulas to compute posterior expectations and variances have been proposed by Tierney, Kass and Kadane (1989). We provide in this article formulas for the multidimensional case. We demonstrate the accuracy of these formulas and show how to use them in importance sampling to design an importance density function which reduces the Monte Carlo error.</abstract><pub>IEEE</pub><tpages>5</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Approximation methods Bayesian methods Computational modeling importance sampling Laplace method Monte Carlo methods Nonlinear Bayesian estimation Numerical models Optimized production technology Probability density function |
title | Multidimensional Laplace formulas for nonlinear Bayesian estimation |
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