D. Hunter, Prabhani Kuruppumullage Don, B. Lindsay
{"title":"An Expansive View of EM Algorithms","authors":"D. Hunter, Prabhani Kuruppumullage Don, B. Lindsay","doi":"10.1201/9780429055911-3","DOIUrl":"https://doi.org/10.1201/9780429055911-3","url":null,"abstract":"","PeriodicalId":12943,"journal":{"name":"Handbook of Mixture Analysis","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81859408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mixture Modelling of High-Dimensional Data","authors":"Damien McParland, T. B. Murphy","doi":"10.1201/9780429055911-11","DOIUrl":"https://doi.org/10.1201/9780429055911-11","url":null,"abstract":"","PeriodicalId":12943,"journal":{"name":"Handbook of Mixture Analysis","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83394842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Applications in Astronomy","authors":"M. Kuhn, E. Feigelson","doi":"10.1201/9780429055911-19","DOIUrl":"https://doi.org/10.1201/9780429055911-19","url":null,"abstract":"","PeriodicalId":12943,"journal":{"name":"Handbook of Mixture Analysis","volume":"29 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72623054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bayesian Mixture Models: Theory and Methods","authors":"J. Rousseau, C. Grazian, J. Lee","doi":"10.1201/9780429055911-4","DOIUrl":"https://doi.org/10.1201/9780429055911-4","url":null,"abstract":"","PeriodicalId":12943,"journal":{"name":"Handbook of Mixture Analysis","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76351655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"EM Methods for Finite Mixtures","authors":"G. Celeux","doi":"10.1201/9780429055911-2","DOIUrl":"https://doi.org/10.1201/9780429055911-2","url":null,"abstract":"HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. EM Methods for Finite Mixtures Gilles Celeux","PeriodicalId":12943,"journal":{"name":"Handbook of Mixture Analysis","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83574230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Applications in Genomics","authors":"S. Robin, C. Ambroise","doi":"10.1201/9780429055911-18","DOIUrl":"https://doi.org/10.1201/9780429055911-18","url":null,"abstract":"","PeriodicalId":12943,"journal":{"name":"Handbook of Mixture Analysis","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89695035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mixtures of Nonparametric Components and Hidden Markov Models","authors":"E. Gassiat","doi":"10.1201/9780429055911-14","DOIUrl":"https://doi.org/10.1201/9780429055911-14","url":null,"abstract":"The topic of this chapter is statistical inference of nonparametric finite mixtures. The latent variables (and thus the observations) will be mostly taken independent and identically distributed, but in some cases, they will be possibly non independently distributed. For each observation, the corresponding latent variable indicates from which population the observation comes from. In particular, when the latent variables form a Markov chain, the observation process will comme from a non parametric hidden Markov model (HMM) with finite state space. We would like to emphasise the fact that the nonparametric modeling will concern only the conditional distribution of the observations, conditional on the latent variables, not the mixing distribution. Nonparametric modeling of the mixing distribution (with possibly infinitely denumerable or continuous support) is considered in Chapter 6. To fix ideas, assume that a random variable X follows a distribution","PeriodicalId":12943,"journal":{"name":"Handbook of Mixture Analysis","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81580553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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