Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation
We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, pr...
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Veröffentlicht in: | Biometrics 2011-03, Vol.67 (1), p.225-233 |
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description | We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra-binomial variation in terms of a zero-one immunity variable, which has a short-lived presence in the host. |
doi_str_mv | 10.1111/j.1541-0420.2010.01410.x |
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The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. 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C.</creatorcontrib><creatorcontrib>Pettitt, A. N.</creatorcontrib><title>Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation</title><title>Biometrics</title><addtitle>Biometrics</addtitle><description>We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra-binomial variation in terms of a zero-one immunity variable, which has a short-lived presence in the host.</description><subject>Animals</subject><subject>Approximate Bayesian computation</subject><subject>Autologistic model</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>BIOMETRIC METHODOLOGY</subject><subject>Biometrics</subject><subject>Brugia pahangi - genetics</subject><subject>Cats - parasitology</subject><subject>Computer Simulation</subject><subject>Descriptive statistics</subject><subject>Evolution, Molecular</subject><subject>Genetics, Population</subject><subject>Host-Parasite Interactions - genetics</subject><subject>Immunity</subject><subject>Inference</subject><subject>Larvae</subject><subject>Macroparasite</subject><subject>Markov analysis</subject><subject>Markov process</subject><subject>Markov processes</subject><subject>Modeling</subject><subject>Models, Genetic</subject><subject>Parameter estimation</subject><subject>Parametric models</subject><subject>Parasite hosts</subject><subject>Parasites</subject><subject>Sequential Monte Carlo</subject><subject>Simulations</subject><subject>Stochastic models</subject><issn>0006-341X</issn><issn>1541-0420</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNUcFu1DAQtRCILqWfALK49JTFduwkviC1y1Jaum0PrUBcLCcZo4QkDnYCu3-Ps2n3wAkf7PG8N8_jNwhhSpY0rPf1kgpOI8IZWTISsoTysG-focUBeI4WhJAkijn9doReeV-HqxSEvURHjMRccJkskFn7oWr1UNkOW4PvtNMtDOA8NtbhjS6c7UPOVwPgO9uPzUxd_7bNuI8efNX9wGd97-x2EgJ8rnfgK93hlW37cdgXvEYvjG48nDyex-jh0_p-9Tm6vr24XJ1dR4VIGYnAmDJnAjghJS94muckKYkoSCIly0shTcZZAoIXWWkKyEMuZ6DjjILQUvD4GJ3OuqGdXyP4QbWVL6BpdAd29CoLz2SZJBPz3T_M2o6uC80FUiJTHjMaSNlMCjZ478Co3oVPup2iRE2TULWaDFeT4WqahNpPQm1D6dtH_TFvoTwUPlkfCB9mwp-qgd1_C6vzy9vNFAaBN7NA7QfrDgKcMs64iAMezXjlB9gecO1-qiSNU6G-3lwoSb_ciKvNd_Ux_gvRJbGP</recordid><startdate>201103</startdate><enddate>201103</enddate><creator>Drovandi, C. C.</creator><creator>Pettitt, A. N.</creator><general>Blackwell Publishing Inc</general><general>Wiley-Blackwell</general><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><scope>7X8</scope></search><sort><creationdate>201103</creationdate><title>Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation</title><author>Drovandi, C. C. ; Pettitt, A. N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5720-effdb25e400d4c47bb06d05c06992bd59f8426e54c8dfceb2bdb2ea381e5a9543</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Animals</topic><topic>Approximate Bayesian computation</topic><topic>Autologistic model</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>BIOMETRIC METHODOLOGY</topic><topic>Biometrics</topic><topic>Brugia pahangi - genetics</topic><topic>Cats - parasitology</topic><topic>Computer Simulation</topic><topic>Descriptive statistics</topic><topic>Evolution, Molecular</topic><topic>Genetics, Population</topic><topic>Host-Parasite Interactions - genetics</topic><topic>Immunity</topic><topic>Inference</topic><topic>Larvae</topic><topic>Macroparasite</topic><topic>Markov analysis</topic><topic>Markov process</topic><topic>Markov processes</topic><topic>Modeling</topic><topic>Models, Genetic</topic><topic>Parameter estimation</topic><topic>Parametric models</topic><topic>Parasite hosts</topic><topic>Parasites</topic><topic>Sequential Monte Carlo</topic><topic>Simulations</topic><topic>Stochastic models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Drovandi, C. C.</creatorcontrib><creatorcontrib>Pettitt, A. N.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><collection>MEDLINE - Academic</collection><jtitle>Biometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Drovandi, C. C.</au><au>Pettitt, A. N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation</atitle><jtitle>Biometrics</jtitle><addtitle>Biometrics</addtitle><date>2011-03</date><risdate>2011</risdate><volume>67</volume><issue>1</issue><spage>225</spage><epage>233</epage><pages>225-233</pages><issn>0006-341X</issn><eissn>1541-0420</eissn><coden>BIOMA5</coden><abstract>We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra-binomial variation in terms of a zero-one immunity variable, which has a short-lived presence in the host.</abstract><cop>Malden, USA</cop><pub>Blackwell Publishing Inc</pub><pmid>20345496</pmid><doi>10.1111/j.1541-0420.2010.01410.x</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Animals Approximate Bayesian computation Autologistic model Bayes Theorem Bayesian analysis BIOMETRIC METHODOLOGY Biometrics Brugia pahangi - genetics Cats - parasitology Computer Simulation Descriptive statistics Evolution, Molecular Genetics, Population Host-Parasite Interactions - genetics Immunity Inference Larvae Macroparasite Markov analysis Markov process Markov processes Modeling Models, Genetic Parameter estimation Parametric models Parasite hosts Parasites Sequential Monte Carlo Simulations Stochastic models |
title | Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation |
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