Nils Lid Hjort , Chris Holmes
نویسندگان
چکیده
The contribution of this book is to collect most recent research of Bayesian nonparametric techniques together, with main emphasis on the use of Dirichlet process. The popularity of Dirichlet process is because that the Dirichlet prior is nonparametric and conjugate, thus presents many opportunities to flexibly model complex data structure. The book incorporates the Bayesian philiosophy into the nonparametric concept. It also introduces Bayesian computational tools (such as the Metropolis-Hasting and Gibbs algorithms) for dealing with possibly infinite number of parameters. A statistical software package in R called DRpackage has been introduced to implement Dirichlet process mixture density estimation, Pólya tree priors for density estimations, nonparametric random effects models including generalized linear models.
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تاریخ انتشار 2010