Alive SMC(2) : Bayesian model selection for low-count time series models with intractable likelihoods

In this article we present a new method for performing Bayesian parameter inference and model choice for low- count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel exact-appr...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Biometrics 2016-06, Vol.72 (2), p.344-353
Hauptverfasser: Drovandi, Christopher C, McCutchan, Roy A
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 353
container_issue 2
container_start_page 344
container_title Biometrics
container_volume 72
creator Drovandi, Christopher C
McCutchan, Roy A
description In this article we present a new method for performing Bayesian parameter inference and model choice for low- count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel exact-approximate algorithm, which we refer to as alive SMC2. The advantages of this approach over competing methods are that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo, and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series, and the cumulative number of prion disease cases in mule deer.
doi_str_mv 10.1111/biom.12449
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_proquest_miscellaneous_1795860727</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1795860727</sourcerecordid><originalsourceid>FETCH-LOGICAL-p196t-b23440c91be8b6c4f1922dbb2cb480cc652d68060b2ccbea4edda25f43cb74903</originalsourceid><addsrcrecordid>eNo1kD1PwzAYhC0kREth4Qcgj2VIsR3HidlKxZdUxADMkT_eqAYnLrFD1X9PpJZbTrp7dMMhdEXJgo661S60C8o4lydoSgtOM8IZmaDzGL8IIbIg7AxNmCgqziidIlh69wv4_XU1Zzf4Dt-rPUSnOtwGCx5H8GCSCx1uQo992GUmDF3CybUwlr2DeCAj3rm0wa5LvTJJaQ_Yu2_wbhOCjRfotFE-wuXRZ-jz8eFj9Zyt355eVst1tqVSpEyznHNiJNVQaWF4QyVjVmtmNK-IMaJgVlREkDExGhQHaxUrGp4bXXJJ8hmaH3a3ffgZIKa6ddGA96qDMMSalrKoBClZOaLXR3TQLdh627tW9fv6_5r8DwBFY8Y</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1795860727</pqid></control><display><type>article</type><title>Alive SMC(2) : Bayesian model selection for low-count time series models with intractable likelihoods</title><source>MEDLINE</source><source>JSTOR Mathematics &amp; Statistics</source><source>Access via Wiley Online Library</source><source>JSTOR Archive Collection A-Z Listing</source><source>Oxford University Press Journals All Titles (1996-Current)</source><creator>Drovandi, Christopher C ; McCutchan, Roy A</creator><creatorcontrib>Drovandi, Christopher C ; McCutchan, Roy A</creatorcontrib><description>In this article we present a new method for performing Bayesian parameter inference and model choice for low- count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel exact-approximate algorithm, which we refer to as alive SMC2. The advantages of this approach over competing methods are that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo, and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series, and the cumulative number of prion disease cases in mule deer.</description><identifier>EISSN: 1541-0420</identifier><identifier>DOI: 10.1111/biom.12449</identifier><identifier>PMID: 26584211</identifier><language>eng</language><publisher>United States</publisher><subject>Algorithms ; Animals ; Bayes Theorem ; Computer Simulation ; Humans ; Iatrogenic Disease ; Markov Chains ; Models, Biological ; Models, Statistical ; Monte Carlo Method ; Prion Diseases ; Time Factors</subject><ispartof>Biometrics, 2016-06, Vol.72 (2), p.344-353</ispartof><rights>2015, The International Biometric Society.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26584211$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Drovandi, Christopher C</creatorcontrib><creatorcontrib>McCutchan, Roy A</creatorcontrib><title>Alive SMC(2) : Bayesian model selection for low-count time series models with intractable likelihoods</title><title>Biometrics</title><addtitle>Biometrics</addtitle><description>In this article we present a new method for performing Bayesian parameter inference and model choice for low- count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel exact-approximate algorithm, which we refer to as alive SMC2. The advantages of this approach over competing methods are that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo, and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series, and the cumulative number of prion disease cases in mule deer.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Bayes Theorem</subject><subject>Computer Simulation</subject><subject>Humans</subject><subject>Iatrogenic Disease</subject><subject>Markov Chains</subject><subject>Models, Biological</subject><subject>Models, Statistical</subject><subject>Monte Carlo Method</subject><subject>Prion Diseases</subject><subject>Time Factors</subject><issn>1541-0420</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNo1kD1PwzAYhC0kREth4Qcgj2VIsR3HidlKxZdUxADMkT_eqAYnLrFD1X9PpJZbTrp7dMMhdEXJgo661S60C8o4lydoSgtOM8IZmaDzGL8IIbIg7AxNmCgqziidIlh69wv4_XU1Zzf4Dt-rPUSnOtwGCx5H8GCSCx1uQo992GUmDF3CybUwlr2DeCAj3rm0wa5LvTJJaQ_Yu2_wbhOCjRfotFE-wuXRZ-jz8eFj9Zyt355eVst1tqVSpEyznHNiJNVQaWF4QyVjVmtmNK-IMaJgVlREkDExGhQHaxUrGp4bXXJJ8hmaH3a3ffgZIKa6ddGA96qDMMSalrKoBClZOaLXR3TQLdh627tW9fv6_5r8DwBFY8Y</recordid><startdate>20160601</startdate><enddate>20160601</enddate><creator>Drovandi, Christopher C</creator><creator>McCutchan, Roy A</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7X8</scope></search><sort><creationdate>20160601</creationdate><title>Alive SMC(2) : Bayesian model selection for low-count time series models with intractable likelihoods</title><author>Drovandi, Christopher C ; McCutchan, Roy A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p196t-b23440c91be8b6c4f1922dbb2cb480cc652d68060b2ccbea4edda25f43cb74903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Animals</topic><topic>Bayes Theorem</topic><topic>Computer Simulation</topic><topic>Humans</topic><topic>Iatrogenic Disease</topic><topic>Markov Chains</topic><topic>Models, Biological</topic><topic>Models, Statistical</topic><topic>Monte Carlo Method</topic><topic>Prion Diseases</topic><topic>Time Factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Drovandi, Christopher C</creatorcontrib><creatorcontrib>McCutchan, Roy A</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><jtitle>Biometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Drovandi, Christopher C</au><au>McCutchan, Roy A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Alive SMC(2) : Bayesian model selection for low-count time series models with intractable likelihoods</atitle><jtitle>Biometrics</jtitle><addtitle>Biometrics</addtitle><date>2016-06-01</date><risdate>2016</risdate><volume>72</volume><issue>2</issue><spage>344</spage><epage>353</epage><pages>344-353</pages><eissn>1541-0420</eissn><abstract>In this article we present a new method for performing Bayesian parameter inference and model choice for low- count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel exact-approximate algorithm, which we refer to as alive SMC2. The advantages of this approach over competing methods are that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo, and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series, and the cumulative number of prion disease cases in mule deer.</abstract><cop>United States</cop><pmid>26584211</pmid><doi>10.1111/biom.12449</doi><tpages>10</tpages></addata></record>
fulltext fulltext
identifier EISSN: 1541-0420
ispartof Biometrics, 2016-06, Vol.72 (2), p.344-353
issn 1541-0420
language eng
recordid cdi_proquest_miscellaneous_1795860727
source MEDLINE; JSTOR Mathematics & Statistics; Access via Wiley Online Library; JSTOR Archive Collection A-Z Listing; Oxford University Press Journals All Titles (1996-Current)
subjects Algorithms
Animals
Bayes Theorem
Computer Simulation
Humans
Iatrogenic Disease
Markov Chains
Models, Biological
Models, Statistical
Monte Carlo Method
Prion Diseases
Time Factors
title Alive SMC(2) : Bayesian model selection for low-count time series models with intractable likelihoods
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T19%3A58%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Alive%20SMC(2)%20:%20Bayesian%20model%20selection%20for%20low-count%20time%20series%20models%20with%20intractable%20likelihoods&rft.jtitle=Biometrics&rft.au=Drovandi,%20Christopher%20C&rft.date=2016-06-01&rft.volume=72&rft.issue=2&rft.spage=344&rft.epage=353&rft.pages=344-353&rft.eissn=1541-0420&rft_id=info:doi/10.1111/biom.12449&rft_dat=%3Cproquest_pubme%3E1795860727%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1795860727&rft_id=info:pmid/26584211&rfr_iscdi=true