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- W4251670728 abstract "This chapter discusses the main formal concepts of Bayesian inference related to hypothesis testing and parametric estimation. Classical procedures are reviewed first, and then Bayesian modifications are suggested allowing us to properly incorporate the prior information. Nonparametric Bayes modeling is a very hot topic in modern statistics. A Bayesian nonparametric model is by definition a Bayesian model on an infinite-dimensional parametric space, which represents the set of all possible solutions for a given problem. The chapter illustrates nonparametric empirical Bayes on the classical compound Poisson example. The continuous version of the Bayes theorem completely solves the problem of representing the posterior through the prior and the likelihood. However the analytical derivation of the posterior is rarely feasible. In particular, such solution is possible if the data distribution obtains a so-called conjugate prior." @default.
- W4251670728 created "2022-05-12" @default.
- W4251670728 date "2017-03-04" @default.
- W4251670728 modified "2023-09-23" @default.
- W4251670728 title "Foundations of Bayesian Analysis" @default.
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- W4251670728 doi "https://doi.org/10.1002/9781118959046.ch2" @default.
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