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- Q17100952 subject Q7483213.
- Q17100952 subject Q7612002.
- Q17100952 abstract "Bayesian hierarchical modeling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. The sub-models combine to form the hierarchical model, and the Bayes’ theorem is used to integrate them with the observed data, and account for all the uncertainty that is present. The result of this integration is the posterior distribution, also known as the updated probability estimate, as additional evidence on the prior distribution is acquired.Frequentist statistics, the more popular foundation of statistics, has been known to contradict Bayesian statistics due to its treatment of the parameters as a random variable, and its use of subjective information in establishing assumptions on these parameters. However, Bayesians argue that relevant information regarding decision making and updating beliefs cannot be ignored and that hierarchical modeling has the potential to overrule classical methods in applications where respondents give multiple observational data. Moreover, the model has proven to be robust, with the posterior distribution less sensitive to the more flexible hierarchical priors.Hierarchical modeling is used when information is available on several different levels of observational units. The hierarchical form of analysis and organization helps in the understanding of multiparameter problems and also plays an important role in developing computational strategies.".
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- Q17100952 wikiPageWikiLink Q1413083.
- Q17100952 wikiPageWikiLink Q161519.
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- Q17100952 wikiPageWikiLink Q278079.
- Q17100952 wikiPageWikiLink Q278090.
- Q17100952 wikiPageWikiLink Q2796622.
- Q17100952 wikiPageWikiLink Q327069.
- Q17100952 wikiPageWikiLink Q3284399.
- Q17100952 wikiPageWikiLink Q3773214.
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- Q17100952 wikiPageWikiLink Q4685361.
- Q17100952 wikiPageWikiLink Q5474576.
- Q17100952 wikiPageWikiLink Q5958522.
- Q17100952 wikiPageWikiLink Q7483213.
- Q17100952 wikiPageWikiLink Q7612002.
- Q17100952 wikiPageWikiLink Q812535.
- Q17100952 comment "Bayesian hierarchical modeling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. The sub-models combine to form the hierarchical model, and the Bayes’ theorem is used to integrate them with the observed data, and account for all the uncertainty that is present.".
- Q17100952 label "Bayesian hierarchical modeling".