Download or read book Higher Order Asymptotics written by J. K. Ghosh and published by IMS. This book was released on 1994 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Application of Higher order Asymptotics in Bayesian Inference written by Richard Stevens and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Probability Matching Priors Higher Order Asymptotics written by Gauri Sankar Datta and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book on the topic of probability matching priors. It targets researchers, Bayesian and frequentist; graduate students in Statistics.
Download or read book An Introduction to Bayesian Analysis written by Jayanta K. Ghosh and published by Springer Science & Business Media. This book was released on 2007-07-03 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior.
Download or read book Inference Asymptotics and Applications written by Nancy Reid and published by World Scientific. This book was released on 2017-03-10 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book showcases the innovative research of Professor Skovgaard, by providing in one place a selection of his most important and influential papers. Introductions by colleagues set in context the highlights, key achievements, and impact, of each work. This book provides a survey of the field of asymptotic theory and inference as it was being pushed forward during an exceptionally fruitful time. It provides students and researchers with an overview of many aspects of the field.
Download or read book Applied Asymptotics written by A. R. Brazzale and published by Cambridge University Press. This book was released on 2007-05-31 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: First practical treatment of small-sample asymptotics, enabling practitioners to apply new methods with confidence.
Download or read book Probability Matching Priors written by Gauri Sankar Datta and published by . This book was released on 2004-01-01 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Inference with Geodetic Applications written by Karl-Rudolf Koch and published by Springer. This book was released on 2006-04-11 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: This introduction to Bayesian inference places special emphasis on applications. All basic concepts are presented: Bayes' theorem, prior density functions, point estimation, confidence region, hypothesis testing and predictive analysis. In addition, Monte Carlo methods are discussed since the applications mostly rely on the numerical integration of the posterior distribution. Furthermore, Bayesian inference in the linear model, nonlinear model, mixed model and in the model with unknown variance and covariance components is considered. Solutions are supplied for the classification, for the posterior analysis based on distributions of robust maximum likelihood type estimates, and for the reconstruction of digital images.
Download or read book Bayesian Inference written by Nicholas G. Polson and published by Edward Elgar Publishing. This book was released on 1995 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two volume set is a collection of 30 classic papers presenting ideas which have now become standard in the field of Bayesian inference. Topics covered include the central field of statistical inference as well as applications to areas of probability theory, information theory, utility theory and computational theory. It is organized into seven sections: foundations, information theory and prior distributions; robustness and outliers; hierarchical, multivariate and non-parametric models; asymptotics; computations and Monte Carlo methods; and Bayesian econometrics.
Download or read book Bayesian Inference written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-07-01 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Bayesian Inference Bayesian inference is a type of statistical inference that updates the probability of a hypothesis based on new data or information using Bayes' theorem. This way of statistical inference is known as the Bayesian method. In the field of statistics, and particularly in the field of mathematical statistics, the Bayesian inference method is an essential tool. When conducting a dynamic analysis of a data sequence, bayesian updating is an especially useful technique to utilize. Inference based on Bayes' theorem has been successfully implemented in a diverse range of fields, including those of science, engineering, philosophy, medicine, athletics, and the legal system. Bayesian inference is strongly related to subjective probability, which is why it is frequently referred to as "Bayesian probability" in the field of decision theory philosophy. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Bayesian Inference Chapter 2: Likelihood Function Chapter 3: Conjugate Prior Chapter 4: Posterior Probability Chapter 5: Maximum a Posteriori Estimation Chapter 6: Bayes Estimator Chapter 7: Bayesian Linear Regression Chapter 8: Dirichlet Distribution Chapter 9: Variational Bayesian Methods Chapter 10: Bayesian Hierarchical Modeling (II) Answering the public top questions about bayesian inference. (III) Real world examples for the usage of bayesian inference in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of bayesian inference' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of bayesian inference.
Download or read book Bayesian Data Analysis Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Frontiers of Statistical Decision Making and Bayesian Analysis written by Ming-Hui Chen and published by Springer Science & Business Media. This book was released on 2010-07-24 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.
Download or read book Bayesian Inference for Stochastic Processes written by Lyle D. Broemeling and published by CRC Press. This book was released on 2017-12-12 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes. It is important that a chapter devoted to the fundamental concepts in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov jump processes, for normal processes (e.g. Brownian motion and the Ornstein–Uhlenbeck process), for traditional time series, and, lastly, for point and spatial processes are described in detail. Heavy emphasis is placed on many examples taken from biology and other scientific disciplines. In order analyses of stochastic processes, it will use R and WinBUGS. Features: Uses the Bayesian approach to make statistical Inferences about stochastic processes The R package is used to simulate realizations from different types of processes Based on realizations from stochastic processes, the WinBUGS package will provide the Bayesian analysis (estimation, testing hypotheses, and prediction) for the unknown parameters of stochastic processes To illustrate the Bayesian inference, many examples taken from biology, economics, and astronomy will reinforce the basic concepts of the subject A practical approach is implemented by considering realistic examples of interest to the scientific community WinBUGS and R code are provided in the text, allowing the reader to easily verify the results of the inferential procedures found in the many examples of the book Readers with a good background in two areas, probability theory and statistical inference, should be able to master the essential ideas of this book.
Download or read book Celebrating Statistics written by A. C. Davison and published by Oxford University Press, USA. This book was released on 2005-09-22 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sir David Cox is among the most important statisticians of the past half-century. He has made pioneering and highly influential contributions to a uniquely wide range of topics in statistics and applied probability. His teaching has inspired generations of students, and many well-known researchers have begun as his graduate students or have worked with him at early stages of their careers. Legions of others have been stimulated and enlightened by the clear, concise, and directexposition exemplified by his many books, papers, and lectures. This book presents a collection of chapters by major statistical researchers who attended a conference held at the University of Neuchatel in July 2004 to celebrate David Cox's 80th birthday. Each chapter is carefully crafted and collectivelypresent current developments across a wide range of research areas from epidemiology, environmental science, finance, computing and medicine.Edited by Anthony Davison, Ecole Polytechnique Federale de Lausanne, Switzerland; Yadolah Dodge, University of Neuchatel, Switzerland; and N. Wermuth, Goteborg University, Sweden, with chapters by Ole E. Barndorff-Nielsen, Sarah C. Darby, Christina Davies, Peter J. Diggle, David Firth, Peter Hall, Valerie S. Isham, Kung-Yee Liang, Peter McCullagh, Paul McGale, Amilcare Porporato, Nancy Reid, Brian D. Ripley, Ignacio Rodriguez-Iturbe, Andrea Rotnitzky, Neil Shephard, Scott L. Zeger, andincluding a brief biography of David Cox, this book is suitable for students of statistics, epidemiology, environmental science, finance, computing and medicine, and academic and practising statisticians.
Download or read book Saddlepoint Approximations with Applications written by Ronald W. Butler and published by Cambridge University Press. This book was released on 2007-08-16 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern statistical methods use complex, sophisticated models that can lead to intractable computations. Saddlepoint approximations can be the answer. Written from the user's point of view, this book explains in clear language how such approximate probability computations are made, taking readers from the very beginnings to current applications. The core material is presented in chapters 1-6 at an elementary mathematical level. Chapters 7-9 then give a highly readable account of higher-order asymptotic inference. Later chapters address areas where saddlepoint methods have had substantial impact: multivariate testing, stochastic systems and applied probability, bootstrap implementation in the transform domain, and Bayesian computation and inference. No previous background in the area is required. Data examples from real applications demonstrate the practical value of the methods. Ideal for graduate students and researchers in statistics, biostatistics, electrical engineering, econometrics, and applied mathematics, this is both an entry-level text and a valuable reference.
Download or read book Bayesian Statistical Inference written by Gudmund R. Iversen and published by SAGE. This book was released on 1984-11 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statisticians now generally acknowledge the theorectical importance of Bayesian inference, if not its practical validity. According to Gudmund R. Iversen, one reason for the lag in applications is that empirical researchers have lacked a grounding in the methodology. His volume provides this introduction and serves as a companion to #4, Tests of Significance.
Download or read book Statistics for the 21st Century written by Gabor Szekely and published by CRC Press. This book was released on 2000-01-25 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: A selection of articles presented at the Eighth Lukacs Symposium held at the Bowling Green State University, Ohio. They discuss consistency and accuracy of the sequential bootstrap, hypothesis testing, geometry in multivariate analysis, the classical extreme value model, the analysis of cross-classified data, diffusion models for neural activity, estimation with quadratic loss, econometrics, higher order asymptotics, pre- and post-limit theorems, and more.
