{"title":"依赖数据的约束蒙特卡罗极大似然","authors":"C. Geyer, E. Thompson","doi":"10.1111/J.2517-6161.1992.TB01443.X","DOIUrl":null,"url":null,"abstract":"Maximum likelihood estimates (MLEs) in autologistic models and other exponential family models for dependent data can be calculated with Markov chain Monte Carlo methods (the Metropolis algorithm or the Gibbs sampler), which simulate ergodic Markov chains having equilibrium distributions in the model. From one realization of such a Markov chain, a Monte Carlo approximant to the whole likelihood function can be constructed. The parameter value (if any) maximizing this function approximates the MLE","PeriodicalId":17425,"journal":{"name":"Journal of the royal statistical society series b-methodological","volume":"126 1","pages":"657-683"},"PeriodicalIF":0.0000,"publicationDate":"1992-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"923","resultStr":"{\"title\":\"Constrained Monte Carlo Maximum Likelihood for Dependent Data\",\"authors\":\"C. Geyer, E. Thompson\",\"doi\":\"10.1111/J.2517-6161.1992.TB01443.X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Maximum likelihood estimates (MLEs) in autologistic models and other exponential family models for dependent data can be calculated with Markov chain Monte Carlo methods (the Metropolis algorithm or the Gibbs sampler), which simulate ergodic Markov chains having equilibrium distributions in the model. From one realization of such a Markov chain, a Monte Carlo approximant to the whole likelihood function can be constructed. The parameter value (if any) maximizing this function approximates the MLE\",\"PeriodicalId\":17425,\"journal\":{\"name\":\"Journal of the royal statistical society series b-methodological\",\"volume\":\"126 1\",\"pages\":\"657-683\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"923\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the royal statistical society series b-methodological\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1111/J.2517-6161.1992.TB01443.X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the royal statistical society series b-methodological","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/J.2517-6161.1992.TB01443.X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Constrained Monte Carlo Maximum Likelihood for Dependent Data
Maximum likelihood estimates (MLEs) in autologistic models and other exponential family models for dependent data can be calculated with Markov chain Monte Carlo methods (the Metropolis algorithm or the Gibbs sampler), which simulate ergodic Markov chains having equilibrium distributions in the model. From one realization of such a Markov chain, a Monte Carlo approximant to the whole likelihood function can be constructed. The parameter value (if any) maximizing this function approximates the MLE
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