{"title":"Testing samples","authors":"","doi":"10.1201/9781315378794-9","DOIUrl":"https://doi.org/10.1201/9781315378794-9","url":null,"abstract":"","PeriodicalId":374287,"journal":{"name":"Basic Concepts in Statistics and Epidemiology","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116686234","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":"Non-parametric statistics","authors":"Iain Ternate","doi":"10.1201/9781315378794-13","DOIUrl":"https://doi.org/10.1201/9781315378794-13","url":null,"abstract":"This Writing be explained an approach in nonparametric Bayesian, a mixture of Polya tree (MPT). MPT uses a partition of supports from a density of its original distribution. In general, the density keeps the original form of the distribution in every mixture of partition as well as adds a new parameter obtained from conditional probabilities. A model of Polya tree can be applied widely and also can be programmed easily by giving MCMC scheme to fit the original parametric model.","PeriodicalId":374287,"journal":{"name":"Basic Concepts in Statistics and Epidemiology","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130361696","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":"Introducing ANOVA","authors":"","doi":"10.1201/9781315378794-18","DOIUrl":"https://doi.org/10.1201/9781315378794-18","url":null,"abstract":"","PeriodicalId":374287,"journal":{"name":"Basic Concepts in Statistics and Epidemiology","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125370221","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":"From Binomial to Poisson","authors":"","doi":"10.1201/9781315378794-12","DOIUrl":"https://doi.org/10.1201/9781315378794-12","url":null,"abstract":"","PeriodicalId":374287,"journal":{"name":"Basic Concepts in Statistics and Epidemiology","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127888538","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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