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Bayesian Inference in Statistical Analysis, by George E. P. Box, George C. Tiao
PDF Ebook Bayesian Inference in Statistical Analysis, by George E. P. Box, George C. Tiao
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Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.
- Sales Rank: #1096520 in Books
- Published on: 1992-04-03
- Original language: English
- Number of items: 1
- Dimensions: 9.05" h x 1.44" w x 6.05" l, 1.77 pounds
- Binding: Paperback
- 608 pages
From the Back Cover
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
Most helpful customer reviews
28 of 28 people found the following review helpful.
classic bayesian text
By Michael R. Chernick
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.
22 of 22 people found the following review helpful.
Bayesian Inference in Statistical Analysis
By A Customer
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.
2 of 2 people found the following review helpful.
Classic of Bayesian Analysis
By Francois Lemay
This book is a classic, particularly with biologists and medical research; less so with physicists. I wish I had read it when I was an undergraduate. I would have wasted less time with Classical methods.
E.T. Jaynes was not overly impressed. In his assessment of this book, He wrote:
"G.E.P. Box is, like L.J.Savage, a curious anomaly in this field; he was assistant to R.A. Fisher and married his daughter, but became a Bayesian in issues of inference while remaining Fisherian in matters of significance tests, which he held to be ouside the ambit of Bayesian methods. In Jaynes (1985), we argue that, on the contrary, any rational significance test requires the full Bayesian apparatus."
I think this is unfair. The book shows its age, but it gives fully Bayesian solutions to many classical problems and it is well written, although the material is dense for a beginner.
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