Practical Nonparametric and Semiparametric Bayesian Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Practical Nonparametric and Semiparametric Bayesian Statistics write by Dipak D. Dey. This book was released on 2012-12-06. Practical Nonparametric and Semiparametric Bayesian Statistics available in PDF, EPUB and Kindle. A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.
Bayesian Non- and Semi-parametric Methods and Applications
Bayesian Non- and Semi-parametric Methods and Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Non- and Semi-parametric Methods and Applications write by Peter Rossi. This book was released on 2014-04-27. Bayesian Non- and Semi-parametric Methods and Applications available in PDF, EPUB and Kindle. This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.
Bayesian Nonparametrics
Bayesian Nonparametrics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Nonparametrics write by Nils Lid Hjort. This book was released on 2010-04-12. Bayesian Nonparametrics available in PDF, EPUB and Kindle. Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
Bayesian Nonparametrics
Bayesian Nonparametrics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Nonparametrics write by J.K. Ghosh. This book was released on 2006-05-11. Bayesian Nonparametrics available in PDF, EPUB and Kindle. 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.
Bayesian Nonparametric Data Analysis
Bayesian Nonparametric Data Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Nonparametric Data Analysis write by Peter Müller. This book was released on 2015-06-17. Bayesian Nonparametric Data Analysis available in PDF, EPUB and Kindle. 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.
