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Bayesian Nonparametrics, Ghosh J.K., Ramamoorthi R.V.



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Автор: Ghosh J.K., Ramamoorthi R.V.
Название:  Bayesian Nonparametrics   (Дж. К. Грош, Р. В. Рамамурти: Байесовские непараметрические расчеты)
Издательство: Springer
Классификация:
Вероятность и статистика

ISBN: 0387955372
ISBN-13(EAN): 9780387955377
ISBN: 0-387-95537-2
ISBN-13(EAN): 978-0-387-95537-7
Обложка/Формат: Hardback
Страницы: 324
Вес: 1.39 кг.
Дата издания: 28.04.2003
Серия: Springer Series in Statistics
Язык: English
Иллюстрации: 4 black & white illustrations, 4 black & white lin
Размер: 24.33 x 15.60 x 2.06
Читательская аудитория: Postgraduate, research & scholarly
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few years. This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics offers a whole continuous spectrum of robust alternatives to purely parametric and purely nonparametric methods of classical statistics. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian nonparametrics. Though the emphasis of the book is on nonparametrics, there is a substantial chapter onJayanta Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently professor of statistics at Purdue University. He has been editor of Sankhya and served on the editorial boards of several journals including the Annals of Statistics. Apart from Bayesian analysis, his interests include asymptotics, stochastic modeling, high dimensional model selection, reliability and survival analysis and bioinformatics.R.V. Ramamoorthi is professor at the Department of Statistics and Probability at Michigan State University. He has published papers in the areas of sufficiency invariance, comparison of experiments, nonparametric survival analysis and Bayesian analysis. In addition to Bayesian nonparametrics, he is currently interested in Bayesian networks and graphical models. He is on the editorial board of Sankhya.
Дополнительное описание: Формат: 235x155
Илюстрации: 4
Круг читателей: Graduate students, researchers
Ключевые слова:
Язык: eng





Principles of Nonparametric Learning

Автор: GyГ¶rfi Laszlo
Название: Principles of Nonparametric Learning
ISBN: 3211836888 ISBN-13(EAN): 9783211836880
Издательство: Springer
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Цена: 14649 р.
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Описание: The book provides systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation and genetic programming.The book is mainly addressed to postgraduates in engineering, mathematics, computer science, and researchers in universities and research institutions.

Bayesian Nonparametrics via Neural Networks

Автор: Herbert K. H. Lee
Название: Bayesian Nonparametrics via Neural Networks
ISBN: 0898715636 ISBN-13(EAN): 9780898715637
Издательство: Eurospan
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Цена: 6442 р.
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Описание: Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. It discusses neural networks in a statistical context, an approach in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and ways to deal with this issue, exploring ideas from statistics and machine learning. An analysis on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, this book will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.

Bayesian Nonparametrics

Автор: Hjort
Название: Bayesian Nonparametrics
ISBN: 0521513464 ISBN-13(EAN): 9780521513463
Издательство: Cambridge Academ
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Цена: 7476 р.
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Описание: 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 Pr?nster, 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.

Nonparametrics / Statistical Methods Based on Ranks

Автор: Lehmann Erich L., D`Abrera H.J.M.
Название: Nonparametrics / Statistical Methods Based on Ranks
ISBN: 0387352120 ISBN-13(EAN): 9780387352121
Издательство: Springer
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Цена: 7314 р.
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Описание: Rank tests form a class of statistical procedures that have the advantage of great simplicity combined with surprising power. Since their development in the 1940s and 1950s, they have taken their place as strong competitors of the more classical normal theory methods. Rank tests apply only to relatively simple solutions, such as one-, tw0-, and s-sample problems, and testing for independence and randomness, but for these situations they are often the method of choice.This reprint of a classic reference book describes these tests and the estimating procedures derived from them, and gives an account of their properties. Even though the field of rank tests has undergone little change, important new methodologies have sprung up that also serve the purpose of freeing statistics from the unrealistic model assumptions that so frequently invalidate its applications. All the tests discussed here are now available in a variety of statistical packages. E.L. Lehmann is Professor of Statistics Emeritus at the University of California, Berkeley. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences, and the recipient of honorary degrees from the University of Leiden, The Netherlands and the University of Chicago. He is the author of Elements of Large-Sample Theory, Theory of Point Estimation, Second Edition (with George Casella), and Testing Statistical Hypotheses, Third Edition (with Joseph P. Romano).

Nonparametric Methods in Change Point Problems

Автор: Brodsky, E., Darkhovsky, B.S.
Название: Nonparametric Methods in Change Point Problems
ISBN: 0792321227 ISBN-13(EAN): 9780792321224
Издательство: Springer
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Цена: 9926 р.
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Описание: This volume deals with non-parametric methods of change point (disorder) detection in random processes and fields. A systematic account is given of up-to-date developments in this rapidly evolving branch of statistics.

Practical Nonparametric and Semiparametric Bayesian Statistics

Автор: Dey
Название: Practical Nonparametric and Semiparametric Bayesian Statistics
ISBN: 0387985174 ISBN-13(EAN): 9780387985176
Издательство: Springer
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Цена: 17241 р.
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Описание: Nonparametric and semiparametric statistical methods are attractive to researchers in a large number of fields, including pharmaceuticals, medical and public health centers, financial institutions, and environmental monitoring centers. This volume presents both the theoretical and applied aspects of these methods.

Fundamentals of Nonparametric Bayesian Inference

Автор: Ghosal, Subhashis.
Название: Fundamentals of Nonparametric Bayesian Inference
ISBN: 0521878268 ISBN-13(EAN): 9780521878265
Издательство: Cambridge Academ
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Цена: 7821 р.
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Описание: Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics.

Bayesian Nonparametric Data Analysis

Автор: Muller, P., Quintana, F.A., Jara, A., Hanson, T.
Название: Bayesian Nonparametric Data Analysis
ISBN: 3319189670 ISBN-13(EAN): 9783319189673
Издательство: Springer
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Цена: 8881 р.
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Описание: This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones.

Nonparametric and Semiparametric Models

Автор: H?rdle
Название: Nonparametric and Semiparametric Models
ISBN: 3540207228 ISBN-13(EAN): 9783540207221
Издательство: Springer
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Цена: 16196 р.
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Описание: The concept of nonparametric smoothing is a central idea in statistics that aims to simultaneously estimate and modes the underlying structure. This book aims to present the statistical and mathematical principles of smoothing with a focus on applicable techniques.

Nonparametric Curve Estimation

Автор: Efromovich
Название: Nonparametric Curve Estimation
ISBN: 0387987401 ISBN-13(EAN): 9780387987408
Издательство: Springer
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Цена: 17241 р.
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Описание: Gives an introduction to nonparametric curve estimation theory.

A Distribution-Free Theory of Nonparametric Regression

Автор: Gy?rfi
Название: A Distribution-Free Theory of Nonparametric Regression
ISBN: 0387954414 ISBN-13(EAN): 9780387954417
Издательство: Springer
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Цена: 18809 р.
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Описание: Presents an approach to nonparametric regression with random design. This monograph is intended for graduate students and researchers in statistics, mathematics, computer science, and engineering.

Nonparametric Smoothing and Lack-of-Fit Tests

Автор: Hart
Название: Nonparametric Smoothing and Lack-of-Fit Tests
ISBN: 0387949801 ISBN-13(EAN): 9780387949802
Издательство: Springer
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Цена: 17241 р.
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Описание: A fundamental problem in statistical analysis is checking how well a particular probability model fits a set of observed data. In many settings, nonparametric smoothing methods provide a convenient and powerful means of testing model fit. Nonparametric Smoothing and Lack-of-Fit Tests explores the use of smoothing methods in testing the fit of parametric regression models.

The book reviews many of the existing methods for testing lack-of-fit and also proposes a number of new methods. Both applied and theoretical aspects of the model checking problems are addressed. As such, the book should be of interest to practitioners of statistics and researchers investigating either lack-of-fit tests or nonparametric smoothing ideas.

The first four chapters of the book are an introduction to the problem of estimating regression functions by nonparametric smoothers, primarily those of kernel and Fourier series type. This part of the book could be used as the foundation for a graduate level course on nonparametric function estimation. The prerequisites for a full appreciation of the book are a modest knowledge of calculus and some familiarity with the basics of mathematical statistics.

The less mathematically sophisticated reader will find Chapter 2 to be a comprehensible introduction to smoothing ideas and the rest of the book to be a valuable reference for both nonparametric function estimation and lack-of-fit tests. Jeffrey D. Hart is Pr fessor of Statistics at Texas A&M University.

He is an associate editor of the Journal of the American Statistical Association, an elected Fellow of the Institute of Mathematical Statistics, and winner of a distinguished teaching award at Texas A&M University.


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