Search Results for "bayesian-theory-wiley-series-in-probability-and-statistics"

Bayesian Theory

Bayesian Theory

  • Author: José M. Bernardo,Adrian F. M. Smith
  • Publisher: John Wiley & Sons
  • ISBN: 047031771X
  • Category: Mathematics
  • Page: 608
  • View: 8365
DOWNLOAD NOW »
This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called prior ignorance . The work is written from the authors s committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critical re-examination of controversial issues. The level of mathematics used is such that most material is accessible to readers with knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. The book will be an ideal source for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics

Case Studies in Bayesian Statistical Modelling and Analysis

Case Studies in Bayesian Statistical Modelling and Analysis

  • Author: Clair L. Alston,Kerrie L. Mengersen,Anthony N. Pettitt
  • Publisher: John Wiley & Sons
  • ISBN: 1118394321
  • Category: Mathematics
  • Page: 504
  • View: 7742
DOWNLOAD NOW »
Provides an accessible foundation to Bayesian analysis usingreal world models This book aims to present an introduction to Bayesian modellingand computation, by considering real case studies drawn fromdiverse fields spanning ecology, health, genetics and finance. Eachchapter comprises a description of the problem, the correspondingmodel, the computational method, results and inferences as well asthe issues that arise in the implementation of theseapproaches. Case Studies in Bayesian Statistical Modelling andAnalysis: Illustrates how to do Bayesian analysis in a clear and concisemanner using real-world problems. Each chapter focuses on a real-world problem and describes theway in which the problem may be analysed using Bayesianmethods. Features approaches that can be used in a wide area ofapplication, such as, health, the environment, genetics,information science, medicine, biology, industry and remotesensing. Case Studies in Bayesian Statistical Modelling andAnalysis is aimed at statisticians, researchers andpractitioners who have some expertise in statistical modelling andanalysis, and some understanding of the basics of Bayesianstatistics, but little experience in its application. Graduatestudents of statistics and biostatistics will also find this bookbeneficial.

Contemporary Bayesian Econometrics and Statistics

Contemporary Bayesian Econometrics and Statistics

  • Author: John Geweke
  • Publisher: John Wiley & Sons
  • ISBN: 0471744727
  • Category: Mathematics
  • Page: 300
  • View: 5094
DOWNLOAD NOW »
Tools to improve decision making in an imperfect world This publication provides readers with a thorough understanding ofBayesian analysis that is grounded in the theory of inference andoptimal decision making. Contemporary Bayesian Econometrics andStatistics provides readers with state-of-the-art simulationmethods and models that are used to solve complex real-worldproblems. Armed with a strong foundation in both theory andpractical problem-solving tools, readers discover how to optimizedecision making when faced with problems that involve limited orimperfect data. The book begins by examining the theoretical and mathematicalfoundations of Bayesian statistics to help readers understand howand why it is used in problem solving. The author then describeshow modern simulation methods make Bayesian approaches practicalusing widely available mathematical applications software. Inaddition, the author details how models can be applied to specificproblems, including: * Linear models and policy choices * Modeling with latent variables and missing data * Time series models and prediction * Comparison and evaluation of models The publication has been developed and fine- tuned through a decadeof classroom experience, and readers will find the author'sapproach very engaging and accessible. There are nearly 200examples and exercises to help readers see how effective use ofBayesian statistics enables them to make optimal decisions. MATLAB?and R computer programs are integrated throughout the book. Anaccompanying Web site provides readers with computer code for manyexamples and datasets. This publication is tailored for research professionals who useeconometrics and similar statistical methods in their work. Withits emphasis on practical problem solving and extensive use ofexamples and exercises, this is also an excellent textbook forgraduate-level students in a broad range of fields, includingeconomics, statistics, the social sciences, business, and publicpolicy.

Bayesian statistics

Bayesian statistics

principles, models, and applications

  • Author: S. James Press
  • Publisher: John Wiley & Sons Inc
  • ISBN: N.A
  • Category: Mathematics
  • Page: 237
  • View: 9335
DOWNLOAD NOW »
An introduction to Bayesian statistics, with emphasis on interpretation of theory, and application of Bayesian ideas to practical problems. First part covers basic issues and principles, such as subjective probability, Bayesian inference and decision making, the likelihood principle, predictivism, and numerical methods of approximating posterior distributions, and includes a listing of Bayesian computer programs. Second part is devoted to models and applications, including univariate and multivariate regression models, the general linear model, Bayesian classification and discrimination, and a case study of how disputed authorship of some of the Federalist Papers was resolved via Bayesian analysis. Includes biographical material on Thomas Bayes, and a reproduction of Bayes's original essay. Contains exercises.

Bayesian Analysis in Statistics and Econometrics

Bayesian Analysis in Statistics and Econometrics

Essays in Honor of Arnold Zellner

  • Author: Donald A. Berry
  • Publisher: John Wiley & Sons
  • ISBN: 9780471118565
  • Category: Business & Economics
  • Page: 577
  • View: 3170
DOWNLOAD NOW »
This book is a definitive work that captures the current state of knowledge of Bayesian Analysis in Statistics and Econometrics and attempts to move it forward. It covers such topics as foundations, forecasting inferential matters, regression, computation and applications.

Subjective and Objective Bayesian Statistics

Subjective and Objective Bayesian Statistics

Principles, Models, and Applications

  • Author: S. James Press
  • Publisher: John Wiley & Sons
  • ISBN: 0470317949
  • Category: Mathematics
  • Page: 600
  • View: 4922
DOWNLOAD NOW »
Shorter, more concise chapters provide flexible coverage of the subject. Expanded coverage includes: uncertainty and randomness, prior distributions, predictivism, estimation, analysis of variance, and classification and imaging. Includes topics not covered in other books, such as the de Finetti Transform. Author S. James Press is the modern guru of Bayesian statistics.

Bayes Linear Statistics

Bayes Linear Statistics

Theory and Methods

  • Author: Michael Goldstein,David Wooff
  • Publisher: John Wiley & Sons
  • ISBN: 9780470065679
  • Category: Mathematics
  • Page: 536
  • View: 6733
DOWNLOAD NOW »
Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data. The methodology differs from the full Bayesian methodology in that it establishes simpler approaches to belief specification and analysis based around expectation judgements. Bayes Linear Statistics presents an authoritative account of this approach, explaining the foundations, theory, methodology, and practicalities of this important field. The text provides a thorough coverage of Bayes linear analysis, from the development of the basic language to the collection of algebraic results needed for efficient implementation, with detailed practical examples. The book covers: The importance of partial prior specifications for complex problems where it is difficult to supply a meaningful full prior probability specification. Simple ways to use partial prior specifications to adjust beliefs, given observations. Interpretative and diagnostic tools to display the implications of collections of belief statements, and to make stringent comparisons between expected and actual observations. General approaches to statistical modelling based upon partial exchangeability judgements. Bayes linear graphical models to represent and display partial belief specifications, organize computations, and display the results of analyses. Bayes Linear Statistics is essential reading for all statisticians concerned with the theory and practice of Bayesian methods. There is an accompanying website hosting free software and guides to the calculations within the book.

Bayesian Statistics and Marketing

Bayesian Statistics and Marketing

  • Author: Peter E. Rossi,Greg M. Allenby,Rob McCulloch
  • Publisher: John Wiley & Sons
  • ISBN: 0470863684
  • Category: Mathematics
  • Page: 368
  • View: 5776
DOWNLOAD NOW »
The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources. Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product purchases and survey data, demand models based on micro-economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods. Written by the leading experts in the field, this unique book: Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models. Provides a self-contained introduction to Bayesian methods. Includes case studies drawn from the authors’ recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems. Is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition the book’s website hosts datasets and R code for the case studies. Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour. It provides a unified reference for cutting-edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike.

Bayesian Theory and Applications

Bayesian Theory and Applications

  • Author: David A. Stephens
  • Publisher: Oxford University Press
  • ISBN: 0199695601
  • Category: Mathematics
  • Page: 702
  • View: 1110
DOWNLOAD NOW »
This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field.

Aspects of Statistical Inference

Aspects of Statistical Inference

  • Author: A. H. Welsh
  • Publisher: John Wiley & Sons
  • ISBN: 9780471115915
  • Category: Mathematics
  • Page: 451
  • View: 9477
DOWNLOAD NOW »
Relevant, concrete, and thorough—the essential data-basedtext on statistical inference The ability to formulate abstract concepts and draw conclusionsfrom data is fundamental to mastering statistics. Aspects ofStatistical Inference equips advanced undergraduate and graduatestudents with a comprehensive grounding in statistical inference,including nonstandard topics such as robustness, randomization, andfinite population inference. A. H. Welsh goes beyond the standard texts and expertlysynthesizes broad, critical theory with concrete data and relevanttopics. The text follows a historical framework, uses real-datasets and statistical graphics, and treats multiparameter problems,yet is ultimately about the concepts themselves. Written with clarity and depth, Aspects of StatisticalInference: Provides a theoretical and historical grounding in statisticalinference that considers Bayesian, fiducial, likelihood, andfrequentist approaches Illustrates methods with real-data sets on diabeticretinopathy, the pharmacological effects of caffeine, stellarvelocity, and industrial experiments Considers multiparameter problems Develops large sample approximations and shows how to usethem Presents the philosophy and application of robustnesstheory Highlights the central role of randomization in statistics Uses simple proofs to illuminate foundational concepts Contains an appendix of useful facts concerning expansions,matrices, integrals, and distribution theory Here is the ultimate data-based text for comparing andpresenting the latest approaches to statistical inference.