Bayesian Method Of Moments

Download Bayesian Method Of Moments full books in PDF, epub, and Kindle. Read online Bayesian Method Of Moments ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Bayesian Data Analysis, Third Edition

Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)

DOWNLOAD NOW!

Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman

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.

Generalized Method of Moments Estimation

Author : Laszlo Matyas
Publisher : Cambridge University Press
ISBN 13 : 9780521669672
Total Pages : 332 pages
Book Rating : 4.6/5 (696 download)

DOWNLOAD NOW!

Book Synopsis Generalized Method of Moments Estimation by : Laszlo Matyas

Download or read book Generalized Method of Moments Estimation written by Laszlo Matyas and published by Cambridge University Press. This book was released on 1999-04-13 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: The generalized method of moments (GMM) estimation has emerged as providing a ready to use, flexible tool of application to a large number of econometric and economic models by relying on mild, plausible assumptions. The principal objective of this volume is to offer a complete presentation of the theory of GMM estimation as well as insights into the use of these methods in empirical studies. It is also designed to serve as a unified framework for teaching estimation theory in econometrics. Contributors to the volume include well-known authorities in the field based in North America, the UK/Europe, and Australia. The work is likely to become a standard reference for graduate students and professionals in economics, statistics, financial modeling, and applied mathematics.

Maximum Entropy and Bayesian Methods

Author : G. Erickson
Publisher : Springer Science & Business Media
ISBN 13 : 9401150281
Total Pages : 300 pages
Book Rating : 4.4/5 (11 download)

DOWNLOAD NOW!

Book Synopsis Maximum Entropy and Bayesian Methods by : G. Erickson

Download or read book Maximum Entropy and Bayesian Methods written by G. Erickson and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume has its origin in the Seventeenth International Workshop on Maximum Entropy and Bayesian Methods, MAXENT 97. The workshop was held at Boise State University in Boise, Idaho, on August 4 -8, 1997. As in the past, the purpose of the workshop was to bring together researchers in different fields to present papers on applications of Bayesian methods (these include maximum entropy) in science, engineering, medicine, economics, and many other disciplines. Thanks to significant theoretical advances and the personal computer, much progress has been made since our first Workshop in 1981. As indicated by several papers in these proceedings, the subject has matured to a stage in which computational algorithms are the objects of interest, the thrust being on feasibility, efficiency and innovation. Though applications are proliferating at a staggering rate, some in areas that hardly existed a decade ago, it is pleasing that due attention is still being paid to foundations of the subject. The following list of descriptors, applicable to papers in this volume, gives a sense of its contents: deconvolution, inverse problems, instrument (point-spread) function, model comparison, multi sensor data fusion, image processing, tomography, reconstruction, deformable models, pattern recognition, classification and group analysis, segmentation/edge detection, brain shape, marginalization, algorithms, complexity, Ockham's razor as an inference tool, foundations of probability theory, symmetry, history of probability theory and computability. MAXENT 97 and these proceedings could not have been brought to final form without the support and help of a number of people.

Introduction to Modern Bayesian Econometrics

Author : Tony Lancaster
Publisher : Wiley-Blackwell
ISBN 13 : 9781405117197
Total Pages : 401 pages
Book Rating : 4.1/5 (171 download)

DOWNLOAD NOW!

Book Synopsis Introduction to Modern Bayesian Econometrics by : Tony Lancaster

Download or read book Introduction to Modern Bayesian Econometrics written by Tony Lancaster and published by Wiley-Blackwell. This book was released on 2004-06-28 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: Almost two hundred and forty years ago, an English clergyman named Thomas Bayes developed a method to calculate the chances of uncertain events. While his method has extensive applications to the work of applied economists, it is only recent advances in computing that have made it possible to exploit the full power of the Bayesian way of doing applied economics.In this new and expanding area, Tony Lancasters text provides a comprehensive introduction to the Bayesian way of doing applied economics. Using clear explanations and practical illustrations and problems, the text presents innovative, computer-intensive ways for applied economists to use the Bayesian method.The Introduction emphasizes computation and the study of probability distributions by computer sampling, showing how these techniques can provide exact inferences about a wide range of econometric problems. Covering all the standard econometric models, including linear and non-linear regression using cross-sectional, time series, and panel data, it also details causal inference and inference about structural econometric models. In addition, each chapter includes numerical and graphical examples and demonstrates their solutions using the S programming language and Bugs software.

Ecological Inference

Author : Gary King
Publisher : Cambridge University Press
ISBN 13 : 9780521542807
Total Pages : 436 pages
Book Rating : 4.5/5 (428 download)

DOWNLOAD NOW!

Book Synopsis Ecological Inference by : Gary King

Download or read book Ecological Inference written by Gary King and published by Cambridge University Press. This book was released on 2004-09-13 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing upon the recent explosion of research in the field, a diverse group of scholars surveys the latest strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays offers many fresh and important contributions to the study of ecological inference.

Introduction to Bayesian Estimation and Copula Models of Dependence

Author : Arkady Shemyakin
Publisher : John Wiley & Sons
ISBN 13 : 1118959019
Total Pages : 314 pages
Book Rating : 4.1/5 (189 download)

DOWNLOAD NOW!

Book Synopsis Introduction to Bayesian Estimation and Copula Models of Dependence by : Arkady Shemyakin

Download or read book Introduction to Bayesian Estimation and Copula Models of Dependence written by Arkady Shemyakin and published by John Wiley & Sons. This book was released on 2017-03-20 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents an introduction to Bayesian statistics, presents an emphasis on Bayesian methods (prior and posterior), Bayes estimation, prediction, MCMC,Bayesian regression, and Bayesian analysis of statistical modelsof dependence, and features a focus on copulas for risk management Introduction to Bayesian Estimation and Copula Models of Dependence emphasizes the applications of Bayesian analysis to copula modeling and equips readers with the tools needed to implement the procedures of Bayesian estimation in copula models of dependence. This book is structured in two parts: the first four chapters serve as a general introduction to Bayesian statistics with a clear emphasis on parametric estimation and the following four chapters stress statistical models of dependence with a focus of copulas. A review of the main concepts is discussed along with the basics of Bayesian statistics including prior information and experimental data, prior and posterior distributions, with an emphasis on Bayesian parametric estimation. The basic mathematical background of both Markov chains and Monte Carlo integration and simulation is also provided. The authors discuss statistical models of dependence with a focus on copulas and present a brief survey of pre-copula dependence models. The main definitions and notations of copula models are summarized followed by discussions of real-world cases that address particular risk management problems. In addition, this book includes: • Practical examples of copulas in use including within the Basel Accord II documents that regulate the world banking system as well as examples of Bayesian methods within current FDA recommendations • Step-by-step procedures of multivariate data analysis and copula modeling, allowing readers to gain insight for their own applied research and studies • Separate reference lists within each chapter and end-of-the-chapter exercises within Chapters 2 through 8 • A companion website containing appendices: data files and demo files in Microsoft® Office Excel®, basic code in R, and selected exercise solutions Introduction to Bayesian Estimation and Copula Models of Dependence is a reference and resource for statisticians who need to learn formal Bayesian analysis as well as professionals within analytical and risk management departments of banks and insurance companies who are involved in quantitative analysis and forecasting. This book can also be used as a textbook for upper-undergraduate and graduate-level courses in Bayesian statistics and analysis. ARKADY SHEMYAKIN, PhD, is Professor in the Department of Mathematics and Director of the Statistics Program at the University of St. Thomas. A member of the American Statistical Association and the International Society for Bayesian Analysis, Dr. Shemyakin's research interests include informationtheory, Bayesian methods of parametric estimation, and copula models in actuarial mathematics, finance, and engineering. ALEXANDER KNIAZEV, PhD, is Associate Professor and Head of the Department of Mathematics at Astrakhan State University in Russia. Dr. Kniazev's research interests include representation theory of Lie algebras and finite groups, mathematical statistics, econometrics, and financial mathematics.

Case Studies in Bayesian Statistics

Author : Constantine Gatsonis
Publisher : Springer Science & Business Media
ISBN 13 : 1461222907
Total Pages : 483 pages
Book Rating : 4.4/5 (612 download)

DOWNLOAD NOW!

Book Synopsis Case Studies in Bayesian Statistics by : Constantine Gatsonis

Download or read book Case Studies in Bayesian Statistics written by Constantine Gatsonis and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: This third volume of case studies presents detailed applications of Bayesian statistical analysis, emphasising the scientific context. The papers were presented and discussed at a workshop held at Carnegie-Mellon University, and this volume - dedicated to the memory of Morrie Groot-reproduces six invited papers, each with accompanying invited discussion, and nine contributed papers with the focus on econometric applications.

Bayesian Econometrics

Author : Siddhartha Chib
Publisher : Emerald Group Publishing
ISBN 13 : 1848553099
Total Pages : 656 pages
Book Rating : 4.8/5 (485 download)

DOWNLOAD NOW!

Book Synopsis Bayesian Econometrics by : Siddhartha Chib

Download or read book Bayesian Econometrics written by Siddhartha Chib and published by Emerald Group Publishing. This book was released on 2008-12-18 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: Illustrates the scope and diversity of modern applications, reviews advances, and highlights many desirable aspects of inference and computations. This work presents an historical overview that describes key contributions to development and makes predictions for future directions.

Maximum Entropy and Bayesian Methods

Author : John Skilling
Publisher : Springer Science & Business Media
ISBN 13 : 9400901070
Total Pages : 327 pages
Book Rating : 4.4/5 (9 download)

DOWNLOAD NOW!

Book Synopsis Maximum Entropy and Bayesian Methods by : John Skilling

Download or read book Maximum Entropy and Bayesian Methods written by John Skilling and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume records papers given at the fourteenth international maximum entropy conference, held at St John's College Cambridge, England. It seems hard to believe that just thirteen years have passed since the first in the series, held at the University of Wyoming in 1981, and six years have passed since the meeting last took place here in Cambridge. So much has happened. There are two major themes at these meetings, inference and physics. The inference work uses the confluence of Bayesian and maximum entropy ideas to develop and explore a wide range of scientific applications, mostly concerning data analysis in one form or another. The physics work uses maximum entropy ideas to explore the thermodynamic world of macroscopic phenomena. Of the two, physics has the deeper historical roots, and much of the inspiration behind the inference work derives from physics. Yet it is no accident that most of the papers at these meetings are on the inference side. To develop new physics, one must use one's brains alone. To develop inference, computers are used as well, so that the stunning advances in computational power render the field open to rapid advance. Indeed, we have seen a revolution. In the larger world of statistics beyond the maximum entropy movement as such, there is now an explosion of work in Bayesian methods, as the inherent superiority of a defensible and consistent logical structure becomes increasingly apparent in practice.

Bayesian Modeling and Computation in Python

Author : Osvaldo A. Martin
Publisher : CRC Press
ISBN 13 : 1000520048
Total Pages : 420 pages
Book Rating : 4.0/5 (5 download)

DOWNLOAD NOW!

Book Synopsis Bayesian Modeling and Computation in Python by : Osvaldo A. Martin

Download or read book Bayesian Modeling and Computation in Python written by Osvaldo A. Martin and published by CRC Press. This book was released on 2021-12-28 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory. The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modelling in different settings, and a chapter about the internals of probabilistic programming languages. Finally the last chapter serves as a reference for the rest of the book by getting closer into mathematical aspects or by extending the discussion of certain topics. This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.

Maximum Entropy and Bayesian Methods

Author : P.F. Fougère
Publisher : Springer Science & Business Media
ISBN 13 : 9400906838
Total Pages : 481 pages
Book Rating : 4.4/5 (9 download)

DOWNLOAD NOW!

Book Synopsis Maximum Entropy and Bayesian Methods by : P.F. Fougère

Download or read book Maximum Entropy and Bayesian Methods written by P.F. Fougère and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume represents the proceedings of the Ninth Annual MaxEnt Workshop, held at Dartmouth College in Hanover, New Hampshire, on August 14-18, 1989. These annual meetings are devoted to the theory and practice of Bayesian Probability and the Maximum Entropy Formalism. The fields of application exemplified at MaxEnt '89 are as diverse as the foundations of probability theory and atmospheric carbon variations, the 1987 Supernova and fundamental quantum mechanics. Subjects include sea floor drug absorption in man, pressures, neutron scattering, plasma equilibrium, nuclear magnetic resonance, radar and astrophysical image reconstruction, mass spectrometry, generalized parameter estimation, delay estimation, pattern recognition, heave responses in underwater sound and many others. The first ten papers are on probability theory, and are grouped together beginning with the most abstract followed by those on applications. The tenth paper involves both Bayesian and MaxEnt methods and serves as a bridge to the remaining papers which are devoted to Maximum Entropy theory and practice. Once again, an attempt has been made to start with the more theoretical papers and to follow them with more and more practical applications. Papers number 29, 30 and 31, by Kesaven, Seth and Kapur, represent a somewhat different, perhaps even "unorthodox" viewpoint, and are included here even though the editor and, indeed many in the audience at Dartmouth, disagreed with their content. I feel that scientific disagreements are essential in any developing field, and often lead to a deeper understanding.

Bayesian Analysis in Statistics and Econometrics

Author : Donald A. Berry
Publisher : John Wiley & Sons
ISBN 13 : 9780471118565
Total Pages : 610 pages
Book Rating : 4.1/5 (185 download)

DOWNLOAD NOW!

Book Synopsis Bayesian Analysis in Statistics and Econometrics by : Donald A. Berry

Download or read book Bayesian Analysis in Statistics and Econometrics written by Donald A. Berry and published by John Wiley & Sons. This book was released on 1996 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Structural Reliability

Author : Yan-Gang Zhao
Publisher : John Wiley & Sons
ISBN 13 : 1119620813
Total Pages : 660 pages
Book Rating : 4.1/5 (196 download)

DOWNLOAD NOW!

Book Synopsis Structural Reliability by : Yan-Gang Zhao

Download or read book Structural Reliability written by Yan-Gang Zhao and published by John Wiley & Sons. This book was released on 2021-03-29 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: STRUCTURAL RELIABILITY Discover a new and innovative approach to structural reliability from two authoritative and accomplished authors The subject of structural reliability, which deals with the problems of evaluating the safety and risk posed by a wide variety of structures, has grown rapidly over the last four decades. And while the First-Order Reliability Method is principally used by most textbooks on this subject, other approaches have identified some of the limitations of that method. In Structural Reliability: Approaches from Perspectives of Statistical Moments, accomplished engineers and authors Yan-Gang Zhao and Dr. Zhao-Hui Lu, deliver a concise and insightful exploration of an alternative and innovative approach to structural reliability. Called the Methods of Moment, the authors’ approach is based on the information of statistical moments of basic random variables and the performance function. The Methods of Moment approach facilitates structural reliability analysis and reliability-based design and can be extended to other engineering disciplines, yielding further insights into challenging problems involving randomness. Readers will also benefit from the inclusion of: A thorough introduction to the measures of structural safety, including uncertainties in structural design, deterministic measures of safety, and probabilistic measures of safety An exploration of the fundamentals of structural reliability theory, including the performance function and failure probability A practical discussion of moment evaluation for performance functions, including moment computation for both explicit and implicit performance functions A concise treatment of direct methods of moment, including the third- and fourth-moment reliability methods Perfect for professors, researchers, and graduate students in civil engineering, Structural Reliability: Approaches from Perspectives of Statistical Moments will also earn a place in the libraries of professionals and students working or studying in mechanical engineering, aerospace and aeronautics engineering, marine and offshore engineering, ship engineering, and applied mechanics.

Bayesian Filtering and Smoothing

Author : Simo Särkkä
Publisher : Cambridge University Press
ISBN 13 : 110703065X
Total Pages : 255 pages
Book Rating : 4.1/5 (7 download)

DOWNLOAD NOW!

Book Synopsis Bayesian Filtering and Smoothing by : Simo Särkkä

Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2013-09-05 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.

Structural Macroeconometrics

Author : David N. DeJong
Publisher : Princeton University Press
ISBN 13 : 069115287X
Total Pages : 440 pages
Book Rating : 4.6/5 (911 download)

DOWNLOAD NOW!

Book Synopsis Structural Macroeconometrics by : David N. DeJong

Download or read book Structural Macroeconometrics written by David N. DeJong and published by Princeton University Press. This book was released on 2011-10-23 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an overview and exploration of methodologies, models, and techniques used to analyze forces shaping national economies. This title presents a range of methods for characterizing and evaluating empirical implications, including calibration exercises, method-of-moment procedures, and likelihood-based procedures, both classical and Bayesian.

Contemporary Bayesian Econometrics and Statistics

Author : John Geweke
Publisher : John Wiley & Sons
ISBN 13 : 0471744727
Total Pages : 322 pages
Book Rating : 4.4/5 (717 download)

DOWNLOAD NOW!

Book Synopsis Contemporary Bayesian Econometrics and Statistics by : John Geweke

Download or read book Contemporary Bayesian Econometrics and Statistics written by John Geweke and published by John Wiley & Sons. This book was released on 2005-10-03 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tools to improve decision making in an imperfect world This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data. The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers understand how and why it is used in problem solving. The author then describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can be applied to specific problems, 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 decade of classroom experience, and readers will find the author's approach very engaging and accessible. There are nearly 200 examples and exercises to help readers see how effective use of Bayesian statistics enables them to make optimal decisions. MATLAB? and R computer programs are integrated throughout the book. An accompanying Web site provides readers with computer code for many examples and datasets. This publication is tailored for research professionals who use econometrics and similar statistical methods in their work. With its emphasis on practical problem solving and extensive use of examples and exercises, this is also an excellent textbook for graduate-level students in a broad range of fields, including economics, statistics, the social sciences, business, and public policy.

Bayesian Methods in Structural Bioinformatics

Author : Thomas Hamelryck
Publisher : Springer
ISBN 13 : 3642272258
Total Pages : 399 pages
Book Rating : 4.6/5 (422 download)

DOWNLOAD NOW!

Book Synopsis Bayesian Methods in Structural Bioinformatics by : Thomas Hamelryck

Download or read book Bayesian Methods in Structural Bioinformatics written by Thomas Hamelryck and published by Springer. This book was released on 2012-03-23 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.