Author: Dipak D. Dey
Publisher: Springer Science & Business Media
ISBN: 1461217326
Category : Mathematics
Languages : en
Pages : 376
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.
Practical Nonparametric and Semiparametric Bayesian Statistics
Author: Dipak D. Dey
Publisher: Springer Science & Business Media
ISBN: 1461217326
Category : Mathematics
Languages : en
Pages : 376
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.
Publisher: Springer Science & Business Media
ISBN: 1461217326
Category : Mathematics
Languages : en
Pages : 376
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.
Bayesian Nonparametric Data Analysis
Author: Peter Müller
Publisher: Springer
ISBN: 3319189689
Category : Mathematics
Languages : en
Pages : 203
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.
Publisher: Springer
ISBN: 3319189689
Category : Mathematics
Languages : en
Pages : 203
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.
Bayesian Nonparametrics
Author: J.K. Ghosh
Publisher: Springer Science & Business Media
ISBN: 0387226540
Category : Mathematics
Languages : en
Pages : 311
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.
Publisher: Springer Science & Business Media
ISBN: 0387226540
Category : Mathematics
Languages : en
Pages : 311
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.
Fundamentals of Nonparametric Bayesian Inference
Author: Subhashis Ghosal
Publisher: Cambridge University Press
ISBN: 0521878268
Category : Business & Economics
Languages : en
Pages : 671
Book Description
Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.
Publisher: Cambridge University Press
ISBN: 0521878268
Category : Business & Economics
Languages : en
Pages : 671
Book Description
Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.
A Comparison of the Bayesian and Frequentist Approaches to Estimation
Author: Francisco J. Samaniego
Publisher: Springer Science & Business Media
ISBN: 1441959416
Category : Mathematics
Languages : en
Pages : 235
Book Description
The main theme of this monograph is “comparative statistical inference. ” While the topics covered have been carefully selected (they are, for example, restricted to pr- lems of statistical estimation), my aim is to provide ideas and examples which will assist a statistician, or a statistical practitioner, in comparing the performance one can expect from using either Bayesian or classical (aka, frequentist) solutions in - timation problems. Before investing the hours it will take to read this monograph, one might well want to know what sets it apart from other treatises on comparative inference. The two books that are closest to the present work are the well-known tomes by Barnett (1999) and Cox (2006). These books do indeed consider the c- ceptual and methodological differences between Bayesian and frequentist methods. What is largely absent from them, however, are answers to the question: “which - proach should one use in a given problem?” It is this latter issue that this monograph is intended to investigate. There are many books on Bayesian inference, including, for example, the widely used texts by Carlin and Louis (2008) and Gelman, Carlin, Stern and Rubin (2004). These books differ from the present work in that they begin with the premise that a Bayesian treatment is called for and then provide guidance on how a Bayesian an- ysis should be executed. Similarly, there are many books written from a classical perspective.
Publisher: Springer Science & Business Media
ISBN: 1441959416
Category : Mathematics
Languages : en
Pages : 235
Book Description
The main theme of this monograph is “comparative statistical inference. ” While the topics covered have been carefully selected (they are, for example, restricted to pr- lems of statistical estimation), my aim is to provide ideas and examples which will assist a statistician, or a statistical practitioner, in comparing the performance one can expect from using either Bayesian or classical (aka, frequentist) solutions in - timation problems. Before investing the hours it will take to read this monograph, one might well want to know what sets it apart from other treatises on comparative inference. The two books that are closest to the present work are the well-known tomes by Barnett (1999) and Cox (2006). These books do indeed consider the c- ceptual and methodological differences between Bayesian and frequentist methods. What is largely absent from them, however, are answers to the question: “which - proach should one use in a given problem?” It is this latter issue that this monograph is intended to investigate. There are many books on Bayesian inference, including, for example, the widely used texts by Carlin and Louis (2008) and Gelman, Carlin, Stern and Rubin (2004). These books differ from the present work in that they begin with the premise that a Bayesian treatment is called for and then provide guidance on how a Bayesian an- ysis should be executed. Similarly, there are many books written from a classical perspective.
Bayesian Learning for Neural Networks
Author: Radford M. Neal
Publisher: Springer Science & Business Media
ISBN: 1461207452
Category : Mathematics
Languages : en
Pages : 194
Book Description
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.
Publisher: Springer Science & Business Media
ISBN: 1461207452
Category : Mathematics
Languages : en
Pages : 194
Book Description
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.
Bayesian Statistics 2
Author: J. M. Bernardo
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 822
Book Description
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 822
Book Description
Theory of Nonparametric Tests
Author: Thorsten Dickhaus
Publisher: Springer
ISBN: 3319763156
Category : Mathematics
Languages : en
Pages : 134
Book Description
This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses.
Publisher: Springer
ISBN: 3319763156
Category : Mathematics
Languages : en
Pages : 134
Book Description
This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses.
Statistical Matching
Author: Susanne Rässler
Publisher: Springer Science & Business Media
ISBN: 1461300533
Category : Mathematics
Languages : en
Pages : 260
Book Description
Government policy questions and media planning tasks may be answered by this data set. It covers a wide range of different aspects of statistical matching that in Europe typically is called data fusion. A book about statistical matching will be of interest to researchers and practitioners, starting with data collection and the production of public use micro files, data banks, and data bases. People in the areas of database marketing, public health analysis, socioeconomic modeling, and official statistics will find it useful.
Publisher: Springer Science & Business Media
ISBN: 1461300533
Category : Mathematics
Languages : en
Pages : 260
Book Description
Government policy questions and media planning tasks may be answered by this data set. It covers a wide range of different aspects of statistical matching that in Europe typically is called data fusion. A book about statistical matching will be of interest to researchers and practitioners, starting with data collection and the production of public use micro files, data banks, and data bases. People in the areas of database marketing, public health analysis, socioeconomic modeling, and official statistics will find it useful.
Nonparametric and Semiparametric Models
Author: Wolfgang Karl Härdle
Publisher: Springer Science & Business Media
ISBN: 364217146X
Category : Mathematics
Languages : en
Pages : 317
Book Description
The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.
Publisher: Springer Science & Business Media
ISBN: 364217146X
Category : Mathematics
Languages : en
Pages : 317
Book Description
The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.