Book Description
This new edition responds to the developments and advances that have taken place in this area over the last few years, and offers the reader a comprehensive, up-to-date overview of Bayesian statistics. Every chapter has been thoroughly revised and updated, as have the exercises at the end of each chapter. Clearly written and offering a wide-ranging introduction to Bayesian statistics, this book will be an essential reference for students, researchers and practitioners in statistics.
Customer Reviews:
O'Hagan's Jewel.......2002-06-08
I found this book to be my constant reference.
Like Tonny's narative style and the part on NIG Priors!
Contains valuable contributions by the author
hard to find elsewhere.
Highly recommended!
Book Description
While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an internet site that provides the code, written in R and WinBUGS, used in many of the previously existing and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration. Major changes from the previous edition: · More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms · Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection · Discussion of computation using both R and WinBUGS · Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web · Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses.
Book Description
This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data.
Key features of the book include:
- Comprehensive coverage of an imporant area for both research and applications.
- Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques.
- Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference.
- Includes a number of applications from the social and health sciences.
- Edited and authored by highly respected researchers in the area.
Customer Reviews:
Infer causality!.......2005-04-14
I would recommend this book to any social scientist who is interested in learning statistical methods that can allow them to say that one thing causes another --- and who doesn't? The classical methods taught to social scientists do not allow you to infer causality, and these methods are actually conceptually a lot easier than the classical methods --- at least, I have a much easier time explaining them to non-statisticians than most regression methods. And now they are not even so hard to implement, especially with new software like Jas Sekhon's match library for R.
I also recommend this book to anyone who is working with these methods already. It is helpful to see others' completed projects on similar topics, and it is impressive to see the breadth of topics that the Rubin Causal Model has been applied to. As statistics books go, it is surprisingly human since they emphasize how many of the authors all belong to this one big Statistical Family, as they call it, and they even have a family tree; also, the introduction including the other titles that they considered for the book made me laugh out loud.
If you do decide to buy the book, I recommend that you froogle it first, since its price distribution is left-skewed with some surprisingly low outliers.
Book Description
The 2nd Edition of Dr. Winkler's classic book, first published in 1972, includes a CD Rom, Perspectives, and updated material. However, the basic concepts of Bayesian inference and decision have not really changed. This book gives a foundation in the concepts, enables readers to understand the results of Bayesian inference and decision, provides tools to model real-world problems and carry out basic analyses, and prepares readers for further exploration.
Customer Reviews:
Bayesian Inference and Decision.......2005-08-21
This is definitely useful for someone going for an MBA or other managerial degree. I would not have been able to do my class assignment without it. I am taking a graduate course in Statistics and this reference may help me achieve an A.
Get Past The Title... This Is A Key Reference Book.......2003-07-22
Not only is this a top notch instruction tool from a well-respected author, but for anyone whose work or interests lay in the risk and decision analysis field, this is a "must have" reference book. It may seem oxymoronic to say that a book on statistical inference, "probability", or Bayesian methods is interesting, but that is exactly what the author has achieved. Winkler makes what many of us may view to be a complex and intimidating topic understandable and enables practical implementation of the concepts. More than just a simple re-printing, the second edition provides updated references, additional readings and assessments of developments in the chapter scope since the first edition.
The initial chapter in the book provides an introduction to probabilistic thought and Bayes' theorem. Following chapters deal with discrete and continuous distributions, decision theory (practical applications...including influencing factors such as utility and subjective probability), Value of Information, and Bayesian approaches to hypothesis testing (quite meaningful for six-sigma thinkers, efficient resource appraisal decisions, and other business applications).
Practitioners will have to get past the rather academic title. If you model or work with uncertainty distributions, dependency, correlation, or contingent portfolio analysis, this book will provide the necessary conceptual understanding that will save you time and allow you to provide a better, more valid product.
The CD that accompanies the book provides standard reference tables and work-throughs for many of the problems/examples. All figures found in the book are included as are all chapter exercises (MS Word format). It also provides trial versions of decision software such as Lumina's Analytica (object based simulation) and TreeAge's Data (decision trees). A series of distribution generators are included as a plus for those of us who have a simulation modeling bent.
There is folklore that is tied this title. So well received was the first edition that rumors persist regarding people resorting to beaten up photocopies of the book. You don't have to do that anymore.
Book Description
The interdisciplinary nature of bioinformatics presents a challenge in integrating concepts, methods, software, and multi-platform data. Although there have been rapid developments in new technology and an inundation of statistical methodology and software for the analysis of microarray gene expression arrays, there exist few rigorous statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data, from medical research and molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical models. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools, and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions.
Book Description
Includes practical elements of matrix theory, continuous multivariate distributions and basic multivariate statistics in the normal distribution; regression and the analysis of variance; factor analysis and latent structure analysis; canonical correlations; stable portfolio analysis; classifications and discrimination models; control in the multivariate linear model; and structuring multivariate populations. 1982 edition.
Customer Reviews:
Applied Multivariate Analysis : Using Bayesian and Frequentist Methods of Inference. Second edition.......2006-02-06
Good quality & delivery, sold at reasonable price
Book Description
The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson The Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences Robert G. Bartle The Elements of Integration and Lebesgue Measure George E. P. Box & George C. Tiao Bayesian Inference in Statistical Analysis R. W. Carter Finite Groups of Lie Type: Conjugacy Classes and Complex Characters R. W. Carter Simple Groups of Lie Type William G. Cochran & Gertrude M. Cox Experimental Designs, Second Edition Richard Courant Differential and Integral Calculus, Volume I Richard Courant Differential and Integral Calculus, Volume II Richard Courant & D. Hilbert Methods of Mathematical Physics, Volume I Richard Courant & D. Hilbert Methods of Mathematical Physics, Volume II D. R. Cox Planning of Experiments Harold S. M. Coxeter Introduction to Geometry, Second Edition Charles W. Curtis & Irving Reiner Representation Theory of Finite Groups and Associative Algebras Charles W. Curtis & Irving Reiner Methods of Representation Theory with Applications to Finite Groups and Orders, Volume I Charles W. Curtis & Irving Reiner Methods of Representation Theory with Applications to Finite Groups and Orders, Volume II Bruno de Finetti Theory of Probability, Volume 1 Bruno de Finetti Theory of Probability, Volume 2 W. Edwards Deming Sample Design in Business Research Amos de Shalit & Herman Feshbach Theoretical Nuclear Physics, Volume 1—Nuclear Structure J. L. Doob Stochastic Processes Nelson Dunford & Jacob T. Schwartz Linear Operators, Part One, General Theory Nelson Dunford & Jacob T. Schwartz Linear Operators, Part Two, Spectral Theory—Self Adjoint Operators in Hilbert Space Nelson Dunford & Jacob T. Schwartz Linear Operators, Part Three, Spectral Operators Herman Feshbach Theoretical Nuclear Physics: Nuclear Reactions Bernard Friedman Lectures on Applications-Oriented Mathematics Phillip Griffiths & Joseph Harris Principles of Algebraic Geometry Gerald J. Hahn & Samuel S. Shapiro Statistical Models in Engineering Morris H. Hansen, William N. Hurwitz & Willim G. Madow Sample Survey Methods and Theory, Volume I—Methods and Applications Morris H. Hansen, William N. Hurwitz & William G. Madow Sample Survey Methods and Theory, Volume II—Theory Peter Henrici Applied and Computational Complex Analysis, Volume 1—Power Series—Integration—Conformal Mapping—Location of Zeros Peter Henrici Applied and Computational Complex Analysis, Volume 2—Special Functions—Integral Transforms—Asymptotics—Continued fractions Peter Henrici Applied and Computational Complex Analysis, Volume 3—Discrete Fourier Analysis—Cauchy Integrals—Construction of Conformal Maps—Univalent Functions Peter Hilton & Yel-Chiang Wu A Course in Modern Algebra Harry Hochstadt Integral Equations Leslie Kish Survey Sampling Shoshichi Kobayashi & Katsumi Nomizu Foundations of Differential Geometry, Volume 1 Shoshichi Kobayashi & Katsumi Nomizu Foundations of Differential Geometry, Volume 2 Erwin O. Kreyszig Introductory Functional Analysis with Applications William H. Louisell Quantum Statistical Properties of Radiation Ali Hasan Nayfeh Introduction to Perturbation Techniques Ali Hasan Nayfeh & Dean T. Mook Nonlinear Oscillations Emanuel Parzen Modern Probability Theory and Its Applications P. M. Prenter Splines and Variational Methods Walter Rudin Fourier Analysis on Groups I. H. Segal Enzyme Kinetics: Behavior and Analysis of Rapid Equilibrium and Steady-State Enzyme Systems C. L. Siegel Topics in Complex Function Theory, Volume I—Elliptic Functions and Uniformization Theory C. L. Siegel Topics in Complex Function Theory, Volume II—Automorphic and Abelian Integrals C. L. Siegel Topics in Complex Function Theory, Volume III—Abelian Functions and Modular Functions of Several Variables J. J. Stoker Differential Geometry J. J. Stoker Water Waves: The Mathematical Theory with Applications J. J. Stoker Nonlinear Vibrations in Mechanical and Electrical Systems
Customer Reviews:
classic text on Bayesian methods.......2001-05-25
This is a book written in 1973 but showing the brilliance and forethought of George Box. Wiley reprinted it in its popular paperback classic series in 1992. The first few chapters introduce Bayesian ideas and show how with noninformative priors the Bayesian results resemble the classical frequentist results. This essentially reviews the work pioneered by Harold Jeffreys.
In the latter chapters more complex problems are introduced including many that do not have nice classical solutions. Box and Tiao show how Bayesian methods contribute ideas that provide new insights into these problems. The discussion of hierarchical models anticipated the developments in Bayesian methods that occurred in the 1990 when the MCMC methods burst onto the scene.
This book is nice for a historical perspective but anyone seriously interested in doing modern Bayesian analysis needs a book that deals with the MCMC methods and there are many nice books available these days.
Bayesian Inference in Statistical Analysis.......2000-08-12
Have you ever wondered about the origins and meaning of statistical concepts? Most of the books on Statistics shy away from this topic, they just throw formulae at you! Not this book. In the first two chapters it goes to great extent to explain a very important concept of noninformative prior. It also states very clearly the differencies between more traditional Sampling Theory approach and Bayesian Analysis. While majority of Statisticians prefer the ideas and notions of Sampling Theory, majority of Scientists and Control System Engeneers are more inclined to use Bayesian Analysis because of its practicality. This book gives a plenty of material on Bayesian Inference and shows how to put theoretical knowledge into practice. It presents the material in transparent and orderly fashion but it requires certain degree of mathematical sophistication. A prerequisite would be any standard text book on Statistical Inference.
Book Description
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.
Customer Reviews:
Difficult to follow and truly learn from........2006-08-31
Iversen presents Bayesian statistics as a better alternative to classical statistics to derive inference, especially hypothesis testing. He denotes that among several advantages Bayesian statistics has the advantage of being much easier to understand and teach. Based on this one paper, I did not find this to be the case. While I learned on my own classical statistics including nonparametric, I quickly got lost here. I got stuck on page 16, as I was not able to replicate the calculations in the next example. I still plowed through the remainder of the book. The bit I understood was mainly due to the commonality between Bayesian and classical statistics.
At this stage, the author has not convinced me why one should substitute classical statistics when conducting inference analysis with Bayesian statistics. Classical statistics is well established in academia and the professions. Bayes' theorem is incredibly powerful in terms of evaluating the efficacy of cancer screening tests. But, the follow up body of Bayesian statistics eludes me so far.
The author's presentation is in part responsible for my confusion on the subject. Formulas descriptions are bizarre. The notations make it unclear if you are dealing with a matrix, a fraction, or an exponent. Additionally, the graphs are poor. The distribution curves are hardly drawn at all. Mean and confidence intervals are absent from such graphs.
Hopefully another book can clearly explain what Bayesian statistics is all about. This one certainly was not the one for me.
Average customer rating:
|
Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 26th International Workshop on Bayesian Inference and Maximum Entropy Methods ... / Mathematical and Statistical Phsyics)
Manufacturer: American Institute of Physics
ProductGroup: Book
Binding: Hardcover
 General
| Earth Sciences
| Science
| Subjects
| Books
Research
| Education
| Science
| Subjects
| Books
Methodology & Statistics
| Experiments, Instruments & Measurement
| Science
| Subjects
| Books
General
| Science
| Subjects
| Books
General
| Physics
| Science
| Subjects
| Books
Mathematical Physics
| Physics
| Science
| Subjects
| Books
Entropy
| Physics
| Science
| Subjects
| Books
General
| Medicine
| Subjects
| Books
General
| Physics
| Professional Science
| Professional & Technical
| Subjects
| Books
Mathematical Physics
| Physics
| Professional Science
| Professional & Technical
| Subjects
| Books
General
| Earth Sciences
| Professional Science
| Professional & Technical
| Subjects
| Books
ASIN: 0735403716 |
Book Description
All papers have been peer-reviewed. The MaxEnt workshops are devoted to Bayesian inference and maximum entropy methods in science and engineering. In addition, this workshop included all aspects of probabilistic inference, such as foundations, techniques, algorithms, and applications. Specific topics included are: information theory; probability theory; quantum systems; source separation; information geometry; Bayesian networks, parametric and non-parametric Bayesian data and image processing; Bayesian computation; entropy computation of Markovian and semi-Markovian processes.
Book Description
This is a classical reprint edition of the original 1971 edition of An Introduction to Bayesian Inference in Economics. This historical volume is an early introduction to Bayesian inference and methodology which still has lasting value for today's statistician and student. The coverage ranges from the fundamental concepts and operations of Bayesian inference to analysis of applications in specific econometric problems and the testing of hypotheses and models.
Books:
- Lasers and Optical Fibers in Medicine (Physical Techniques in Biology and Medicine)
- Little, Brown Essential Handbook, The (5th Edition)
- Many-Particle Physics (Physics of Solids and Liquids)
- Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics)
- Mathematical Methods of Classical Mechanics (Graduate Texts in Mathematics)
- Medical Firsts: From Hippocrates to the Human Genome
- Men's Health: The Book of Muscle--The World's Most Authoritative Guide to Building Your Body
- Modern Communications Jamming Principles and Techniques (The Artech House Information Warfare Library)
- Modern Magnetic Materials: Principles and Applications
- Molecular Gas Dynamics and the Direct Simulation of Gas Flows (Oxford Engineering Science Series)
Books Index
Books Home
Recommended Books
- Tax-Aware Investment Management: The Essential Guide
- Living With Blind Dogs: A Resource Book and Training Guide for the Owners of Blind and Low-Vision Do
- Help Is On Its Way: A True Story
- Homage to Catalonia
- Noel Coward: A Biography of No L Coward
- Pale Fire
- Molecular Biology of the Cell, Fourth Edition
- Time Management is an Oxymoron
- Good Company: Caring As Fiercely As You Compete
- Excellence in Direct Marketing Graphics: The International Echo Awards
Kendall's Advanced Theory of Statistics:Volume 2A -Classical Inference and and the Linear Model (Kendall's Library of Statistics)
Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory
Bayesian Theory (Wiley Series in Probability and Statistics)
Data Analysis Using Regression and Multilevel/Hierarchical Models
Bayesian Forecasting and Dynamic Models (Springer Series in Statistics)
