{"title":"BMRMM:贝叶斯马尔可夫(更新)混合模型 R 软件包","authors":"Yutong Wu, Abhra Sarkar","doi":"arxiv-2409.10835","DOIUrl":null,"url":null,"abstract":"We introduce the BMRMM package implementing Bayesian inference for a class of\nMarkov renewal mixed models which can characterize the stochastic dynamics of a\ncollection of sequences, each comprising alternative instances of categorical\nstates and associated continuous duration times, while being influenced by a\nset of exogenous factors as well as a 'random' individual. The default setting\nflexibly models the state transition probabilities using mixtures of Dirichlet\ndistributions and the duration times using mixtures of gamma kernels while also\nallowing variable selection for both. Modeling such data using simpler Markov\nmixed models also remains an option, either by ignoring the duration times\naltogether or by replacing them with instances of an additional category\nobtained by discretizing them by a user-specified unit. The option is also\nuseful when data on duration times may not be available in the first place. We\ndemonstrate the package's utility using two data sets.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"42 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"BMRMM: An R Package for Bayesian Markov (Renewal) Mixed Models\",\"authors\":\"Yutong Wu, Abhra Sarkar\",\"doi\":\"arxiv-2409.10835\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce the BMRMM package implementing Bayesian inference for a class of\\nMarkov renewal mixed models which can characterize the stochastic dynamics of a\\ncollection of sequences, each comprising alternative instances of categorical\\nstates and associated continuous duration times, while being influenced by a\\nset of exogenous factors as well as a 'random' individual. The default setting\\nflexibly models the state transition probabilities using mixtures of Dirichlet\\ndistributions and the duration times using mixtures of gamma kernels while also\\nallowing variable selection for both. Modeling such data using simpler Markov\\nmixed models also remains an option, either by ignoring the duration times\\naltogether or by replacing them with instances of an additional category\\nobtained by discretizing them by a user-specified unit. The option is also\\nuseful when data on duration times may not be available in the first place. We\\ndemonstrate the package's utility using two data sets.\",\"PeriodicalId\":501425,\"journal\":{\"name\":\"arXiv - STAT - Methodology\",\"volume\":\"42 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - STAT - Methodology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.10835\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Methodology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10835","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
BMRMM: An R Package for Bayesian Markov (Renewal) Mixed Models
We introduce the BMRMM package implementing Bayesian inference for a class of
Markov renewal mixed models which can characterize the stochastic dynamics of a
collection of sequences, each comprising alternative instances of categorical
states and associated continuous duration times, while being influenced by a
set of exogenous factors as well as a 'random' individual. The default setting
flexibly models the state transition probabilities using mixtures of Dirichlet
distributions and the duration times using mixtures of gamma kernels while also
allowing variable selection for both. Modeling such data using simpler Markov
mixed models also remains an option, either by ignoring the duration times
altogether or by replacing them with instances of an additional category
obtained by discretizing them by a user-specified unit. The option is also
useful when data on duration times may not be available in the first place. We
demonstrate the package's utility using two data sets.