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Author: J.K. Ghosh
Publisher: Springer Science & Business Media
ISBN: 0387226540
Category : Mathematics
Languages : en
Pages : 311
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Book Description
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: J.K. Ghosh
Publisher: Springer Science & Business Media
ISBN: 0387226540
Category : Mathematics
Languages : en
Pages : 311
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Book Description
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: Nils Lid Hjort
Publisher: Cambridge University Press
ISBN: 1139484605
Category : Mathematics
Languages : en
Pages : 309
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Book Description
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.
Author: Peter Müller
Publisher: Springer
ISBN: 3319189689
Category : Mathematics
Languages : en
Pages : 203
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Book Description
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: Nizar Bouguila
Publisher: Springer
ISBN: 3030238768
Category : Technology & Engineering
Languages : en
Pages : 356
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Book Description
This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature. Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection; Present theoretical and practical developments in mixture-based modeling and their importance in different applications; Discusses perspectives and challenging future works related to mixture modeling.
Author: Dipak D. Dey
Publisher: Springer Science & Business Media
ISBN: 1461217326
Category : Mathematics
Languages : en
Pages : 376
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Book Description
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.
Author: Paul Kay
Publisher: Center for the Study of Language and Information Publica Tion
ISBN: 9781575864167
Category : Colors, Words for
Languages : en
Pages : 0
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Book Description
The 1969 publication of Brent Berlin and Paul Kay's Basic Color Terms proved explosive and controversial. Contrary to the then-popular doctrine of random language variation, Berlin and Kay's multilingual study of color nomenclature indicated a cross-cultural and almost universal pattern in the selection of colors that received abstract names in each language. The ensuing debate helped reform the views of anthropologists, linguists, and psychologists alike. After four decades in print, Basic Color Terms now has a sequel: in this book, the authors authoritatively extend the original survey, studying 110 additional unwritten languages in detail and in situ. The results are presented with charts showing the overall palette of color terms within each language as well as the levels of agreement among speakers.
Author: Kim-Anh Do
Publisher: Cambridge University Press
ISBN: 052186092X
Category : Mathematics
Languages : en
Pages : 437
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Book Description
Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.
Author: Peter Rossi
Publisher: Princeton University Press
ISBN: 0691145326
Category : Business & Economics
Languages : en
Pages : 218
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Book Description
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.
Author: Geoffrey McLachlan
Publisher: John Wiley & Sons
ISBN: 047165406X
Category : Mathematics
Languages : en
Pages : 419
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Book Description
An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.
Author: Subhashis Ghosal
Publisher: Cambridge University Press
ISBN: 0521878268
Category : Business & Economics
Languages : en
Pages : 671
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Book Description
Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.
