Bayesian Nonparametrics

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Bayesian Nonparametrics

Nils Lid Hjort,Chris Holmes,Peter Müller,Stephen G. Walker
Bayesian Nonparametrics Book PDF

Author : Nils Lid Hjort,Chris Holmes,Peter Müller,Stephen G. Walker
Publisher : Cambridge University Press
Page : 309 pages
File Size : 41,5 Mb
Release : 2010-04-12
Category : Mathematics
ISBN : 9781139484602

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Bayesian Nonparametrics by Nils Lid Hjort,Chris Holmes,Peter Müller,Stephen G. Walker Pdf

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

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

Author : J.K. Ghosh,R.V. Ramamoorthi
Publisher : Springer Science & Business Media
Page : 308 pages
File Size : 46,8 Mb
Release : 2006-05-11
Category : Mathematics
ISBN : 9780387226545

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

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

Peter Müller,Fernando Andres Quintana,Alejandro Jara,Tim Hanson
Bayesian Nonparametric Data Analysis Book PDF

Author : Peter Müller,Fernando Andres Quintana,Alejandro Jara,Tim Hanson
Publisher : Springer
Page : 193 pages
File Size : 40,5 Mb
Release : 2015-06-17
Category : Mathematics
ISBN : 9783319189680

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Bayesian Nonparametric Data Analysis by Peter Müller,Fernando Andres Quintana,Alejandro Jara,Tim Hanson Pdf

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.

Bayesian Nonparametrics via Neural Networks

Herbert K. H. Lee
Bayesian Nonparametrics via Neural Networks Book PDF

Author : Herbert K. H. Lee
Publisher : SIAM
Page : 106 pages
File Size : 52,8 Mb
Release : 2004-01-01
Category : Mathematics
ISBN : 0898718422

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Bayesian Nonparametrics via Neural Networks by Herbert K. H. Lee Pdf

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. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is 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 methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.

Fundamentals of Nonparametric Bayesian Inference

Subhashis Ghosal,Aad van der Vaart
Fundamentals of Nonparametric Bayesian Inference Book PDF

Author : Subhashis Ghosal,Aad van der Vaart
Publisher : Cambridge University Press
Page : 671 pages
File Size : 50,8 Mb
Release : 2017-06-26
Category : Business & Economics
ISBN : 9780521878265

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Fundamentals of Nonparametric Bayesian Inference by Subhashis Ghosal,Aad van der Vaart Pdf

Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.

Nonparametric Bayesian Inference in Biostatistics

Riten Mitra,Peter Müller
Nonparametric Bayesian Inference in Biostatistics Book PDF

Author : Riten Mitra,Peter Müller
Publisher : Springer
Page : 448 pages
File Size : 50,7 Mb
Release : 2015-07-25
Category : Medical
ISBN : 9783319195186

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Nonparametric Bayesian Inference in Biostatistics by Riten Mitra,Peter Müller Pdf

As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve.

All of Nonparametric Statistics

Larry Wasserman
All of Nonparametric Statistics Book PDF

Author : Larry Wasserman
Publisher : Springer Science & Business Media
Page : 272 pages
File Size : 43,5 Mb
Release : 2006-09-10
Category : Mathematics
ISBN : 9780387306230

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All of Nonparametric Statistics by Larry Wasserman Pdf

This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.

Nonparametric Statistical Methods

Myles Hollander,Douglas A. Wolfe,Eric Chicken
Nonparametric Statistical Methods Book PDF

Author : Myles Hollander,Douglas A. Wolfe,Eric Chicken
Publisher : John Wiley & Sons
Page : 978 pages
File Size : 53,9 Mb
Release : 2013-11-25
Category : Mathematics
ISBN : 9781118553299

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Nonparametric Statistical Methods by Myles Hollander,Douglas A. Wolfe,Eric Chicken Pdf

Praise for the Second Edition “This book should be an essential part of the personal library of every practicing statistician.”—Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation. Written by leading statisticians, Nonparametric Statistical Methods, Third Edition provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the Third Edition features: The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.

Bayesian Nonparametrics for Causal Inference and Missing Data

Michael J. Daniels,Antonio Linero,Jason Roy
Bayesian Nonparametrics for Causal Inference and Missing Data Book PDF

Author : Michael J. Daniels,Antonio Linero,Jason Roy
Publisher : CRC Press
Page : 263 pages
File Size : 50,6 Mb
Release : 2023-08-23
Category : Mathematics
ISBN : 9781000927719

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Bayesian Nonparametrics for Causal Inference and Missing Data by Michael J. Daniels,Antonio Linero,Jason Roy Pdf

Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest. The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials. Features • Thorough discussion of both BNP and its interplay with causal inference and missing data • How to use BNP and g-computation for causal inference and non-ignorable missingness • How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions • Detailed case studies illustrating the application of BNP methods to causal inference and missing data • R code and/or packages to implement BNP in causal inference and missing data problems The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.

Bayesian Data Analysis, Third Edition

Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Bayesian Data Analysis Third Edition Book PDF

Author : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Publisher : CRC Press
Page : 677 pages
File Size : 52,7 Mb
Release : 2013-11-01
Category : Mathematics
ISBN : 9781439840955

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Bayesian Data Analysis, Third Edition by Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin Pdf

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Bayesian Time Series Models

David Barber,A. Taylan Cemgil,Silvia Chiappa
Bayesian Time Series Models Book PDF

Author : David Barber,A. Taylan Cemgil,Silvia Chiappa
Publisher : Cambridge University Press
Page : 432 pages
File Size : 47,6 Mb
Release : 2011-08-11
Category : Computers
ISBN : 9780521196765

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Bayesian Time Series Models by David Barber,A. Taylan Cemgil,Silvia Chiappa Pdf

The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.

Asymptotics, Nonparametrics, and Time Series

Subir Ghosh
Asymptotics Nonparametrics and Time Series Book PDF

Author : Subir Ghosh
Publisher : CRC Press
Page : 864 pages
File Size : 55,7 Mb
Release : 1999-02-18
Category : Mathematics
ISBN : 0824700511

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Asymptotics, Nonparametrics, and Time Series by Subir Ghosh Pdf

"Contains over 2500 equations and exhaustively covers not only nonparametrics but also parametric, semiparametric, frequentist, Bayesian, bootstrap, adaptive, univariate, and multivariate statistical methods, as well as practical uses of Markov chain models."

Bayesian Statistics and New Generations

Raffaele Argiento,Daniele Durante,Sara Wade
Bayesian Statistics and New Generations Book PDF

Author : Raffaele Argiento,Daniele Durante,Sara Wade
Publisher : Springer Nature
Page : 184 pages
File Size : 47,6 Mb
Release : 2019-11-21
Category : Mathematics
ISBN : 9783030306113

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Bayesian Statistics and New Generations by Raffaele Argiento,Daniele Durante,Sara Wade Pdf

This book presents a selection of peer-reviewed contributions to the fourth Bayesian Young Statisticians Meeting, BAYSM 2018, held at the University of Warwick on 2-3 July 2018. The meeting provided a valuable opportunity for young researchers, MSc students, PhD students, and postdocs interested in Bayesian statistics to connect with the broader Bayesian community. The proceedings offer cutting-edge papers on a wide range of topics in Bayesian statistics, identify important challenges and investigate promising methodological approaches, while also assessing current methods and stimulating applications. The book is intended for a broad audience of statisticians, and demonstrates how theoretical, methodological, and computational aspects are often combined in the Bayesian framework to successfully tackle complex problems.

A Parametric Approach to Nonparametric Statistics

Mayer Alvo,Philip L. H. Yu
A Parametric Approach to Nonparametric Statistics Book PDF

Author : Mayer Alvo,Philip L. H. Yu
Publisher : Springer
Page : 279 pages
File Size : 48,9 Mb
Release : 2018-10-12
Category : Mathematics
ISBN : 9783319941530

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A Parametric Approach to Nonparametric Statistics by Mayer Alvo,Philip L. H. Yu Pdf

This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions to not only derive well-known tests but to also go beyond and make use of Bayesian methods to analyze ranking data. The book bridges the gap between parametric and nonparametric statistics and presents the best practices of the former while enjoying the robustness properties of the latter. This book can be used in a graduate course in nonparametrics, with parts being accessible to senior undergraduates. In addition, the book will be of wide interest to statisticians and researchers in applied fields.

Practical Nonparametric and Semiparametric Bayesian Statistics

Dipak D. Dey,Peter MüIler,Debajyoti Sinha
Practical Nonparametric and Semiparametric Bayesian Statistics Book PDF

Author : Dipak D. Dey,Peter MüIler,Debajyoti Sinha
Publisher : Springer Science & Business Media
Page : 376 pages
File Size : 48,9 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461217329

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Practical Nonparametric and Semiparametric Bayesian Statistics by Dipak D. Dey,Peter MüIler,Debajyoti Sinha Pdf

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.