[PDF] Bayesian Density Estimation And Classification Of Incomplete Data Using Semi Parametric And Non Parametric Models eBook

Author : Peter Müller
Publisher : Springer
Page : 203 pages
File Size : 15,48 MB
Release : 2015-06-17
Category : Mathematics
ISBN : 3319189689

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This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.

Author : J.K. Ghosh
Publisher : Springer Science & Business Media
Page : 311 pages
File Size : 46,33 MB
Release : 2006-05-11
Category : Mathematics
ISBN : 0387226540

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This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.

Author : Abel Rodriguez
Publisher : LAP Lambert Academic Publishing
Page : 168 pages
File Size : 24,27 MB
Release : 2009-03
Category : Bayesian statistical decision theory
ISBN : 9783838300122

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Bayesian nonparametric and semiparametric mixture models have become extremely popular in the last 10 years because they provide flexibility and interpretability while preserving computational simplicity. This book is a contribution to this growing literature, discussing the design of models for collections of distributions and their application to density estimation and nonparametric regression. All methods introduced in this book are discussed in the context of complex scientific applications in public health, epidemiology and finance.

Author : Luc Devroye
Publisher : New York ; Toronto : Wiley
Page : 376 pages
File Size : 14,34 MB
Release : 1985-01-18
Category : Mathematics
ISBN :

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This book gives a rigorous, systematic treatment of density estimates, their construction, use and analysis with full proofs. It develops L1 theory, rather than the classical L2, showing how L1 exposes fundamental properties of density estimates masked by L2.

Author : Yanyi Wang (Mathematician)
Publisher :
Page : 30 pages
File Size : 31,87 MB
Release : 2018
Category : Electronic dissertations
ISBN :

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In modern data analysis, nonparametric Bayesian methods have become increasingly popular. These methods can solve many important statistical inference problems, such as density estimation, regression and survival analysis. In this thesis, We utilize several nonparametric Bayesian methods for density estimation. In particular, we use mixtures of Dirichlet processes (MDP) and mixtures of Polya trees (MPT) priors to perform Bayesian density estimation based on simulated data. The target density is a mixture of normal distributions, which makes the estimation problem non-trivial. The performance of these methods with frequentist nonparametric kernel density estimators is assessed according to a mean-square error criterion. For the cases we consider, the nonparametric Bayesian methods outperform their frequentist counterpart.