Overview

This book was the textbook used at the University of Wisconsin-Madison for the graduate course in Bayesian Decision and Control I during the fall of 2001 and 2002. It strikes a good balance between theory and practical example, making it ideal for a first course in Bayesian theory at an intermediate-advanced graduate level. Its emphasis is on Bayesian modeling and to some degree computation.

Prerequisites

While no Bayesian theory is assumed, it is assumed that the reader has a background in mathematical statistics, probability and continuous multi-variate distributions at a beginning or intermediate graduate level. The mathematics used in the book is basic probability and statistics, elementary calculus and linear algebra.

Intended audience

This book is primarily for graduate students, statisticians and applied researchers who wish to learn Bayesian methods as opposed to the more classical frequentist methods.

Material covered

It covers the fundamentals starting from first principles, single-parameter models, multi-parameter models, large sample inference, hierarchical models, model checking and sensitivity analysis, study design, regression models, generalized linear models, mixture models and models for missing data. In addition it covers posterior simulation and integration using rejection sampling and importance sampling. There is one chapter on Markov chain simulation (MCMC) covering the generalized Metropolis algorithm and the Gibbs sampler.

Over 38 models are covered, 33 detailed examples from a wide range of fields (especially biostatistics). Each of the 18 chapter has a bibliographic note at the end. There are two appendixes: A) a very helpful list of standard probability distributions and B) outline of proofs of asymptotic theorems.

Sixteen of the 18 chapters end with a set of exercises that range from easy to quite difficult. Most of the students in my fall 2001 class used the statistical language R to do the exercises.

The book's emphasis is on applied Bayesian analysis. There are no heavy advanced proofs in the book. While the proofs of the basic algorithms are covered there are no algorithms written in pseudo code...Additional books of related interest

1) Statistical Decision Theory and Bayesian Analysis, James Berger, second edition. Emphasis on decision theory and more difficult to follow than Gelman's book. Covers empirical and hierarchical Bayes analysis. More philosophical challenging than Gelman's book.

2) Monte Carlo Statistical Methods, Robert and Casella. Very mathematically oriented book. Does a good job of covering MCMC.

3) Monte Carlo Methods in Bayesian Computation, Ming-Hui Chen, Qi-Man Shao, Joseph George Ibrahim. An enormous number of algorithms related to MCMC not covered elsewhere. If you need MCMC and need an algorithm to implement MCMC this is the book to read.

4) Monte Carlo Strategies in Scientific Computing, Jun S. Liu. Covers a wide range of scientific disciplines and how Monte Carlo methods can be used to solve real world problems. Includes hot topics such as bioinformatics. Very concise. Well written, but requires effort to understand as so many different topics are covered. This book is my most often borrowed book on Monte Carlo methods. Jun S. Liu is a big gun at Harvard.

5) Probabilistic Networks and Expert Systems. Cowell, Dawid, Lauritzen, Spiegelhalter. Covers the theory and methodology of building Bayesian networks (probabilistic networks).

This is a well written text that is fast becoming a classic reference. It contains a wealth of good applications. It is one of the new books that presents the growing use of Bayesian methods in practice since the advancement of Markov Chain Monte Carlo approach. It includes a whole chapter the Markov chain approach to computation. Other strengths of the book include the chapter on missing data and the chapter that provides expert advice.

Another text in the CRC series Markov Chain Monte Carlo in Practice by Gilks, Richardson and Spiegelhalter provides more detail on these methods along with many applications including some Bayesian ones.

First, I must admit a bias: I frequently work with one of the authors (Gelman), and I think highly of his work and statistical judgment.

This book's biggest strength is its introduction of most of the important ideas in Bayesian statistics through well-chosen examples. These are examples are not contrived: many of them came up in research by the authors over the past several years. Most examples follow a logical progression that was probably used in the original research: a simple model is fit to data; then areas of model mis-fit are sought, and a revised model is used to address them. This brings up another strength of the book: the discussion and treatment of measures of model fit (and sensitivity of inferences) is lucid and enlightening.

Some readers may wish the computational methods were spelled out more fully: this book will help you choose an appropriate statistical model, and the ways to look for serious violations of it, but it will take a bit of work to convert the ideas into computational algorithms. This is not to say that the computational methods aren't discussed, merely that many of the details are left to the reader. The reader expecting pseudo-code programs will be disappointed.

All in all, I recommend this book for anyone who applies statistical models to data, whether those models are Bayesian or not. I especially recommend it for researchers who are curious about Bayesian methods but do not see the point of them---Chapter 5, and particularly section 5.5 (an example chosen from educational testing), beautifully addresses this issue.

This collection of writings by some renowned psychotherapists, including Jean Houston and James Hillman, integrates the spiritual, creative, and psychological aspects of psychotherapy in the tradition of C.G. Jung. The authors skillfully draw upon the perennial spiritual traditions, as well as creative arts, mythology, and mysticism and integrate them with intelligence and grace into a psychology of the soul.

When Europe and the West, including Russia were surged by new movements like Fauvism and Cubism, i thought that the spirit of Impressionism and its brevity were lost in the midst of the new trend in the art scene. Through this book i came to understand sequel story behind Impressionism when it was introduced to the States in the early 1900s. A must-read for any die-hard Impressionists out there who seeks to understand the culture and history set by the pioneering American Impressionists.

This book is absolutely FANTASTIC!!!! Very pleased with it. There are 102 pages in this softcover book and many color reproductions of California Impressionists works of art to include: Wendt, Payne, Bischoff, Gamble, Rose, Hills, and many more. About 60 pages of color photos of the above listed artists and more. Chapter titles include; Impressionism's Indian Summer, From Giverny to Laguna Beach, The California Style In Perspective. The bottom line....I'm VERY happy that I bought this book and recommend it to others. Enjoy

I got curious about California impressionism a few years ago when someone saw my sketchbook and pointed out that what I was doing was called "plein air" art. One of my favorite things to do is to go up into the hills over Berkeley with my sketchbook and get pastel dust all over my nose and on some paper. I poked around for quite a while to find more images of this style but felt like the images I was seeing were not the real deal. They looked a bit flat and dull.

I had the good fortune of seeing an exhibit in Oakland, California at the museum there and got hooked on the luminous colors in the paintings. I managed to view the collections in Irvine and Laguna beach. I don't recall where I got this book, but I took it with me to Laguna Beach and held the pages next to some of the originals.

This books is the only book I have seen that comes even close to reproducing the beautiful and luminous quality of this genre of painting. For me, this book is helpful for those occasions when I struggle with picking a color in one of my own works that hits the quality of light I am trying to arrive at. Plus, it is wonderful to just sit back and say, "Wow."

I will be the first to admit that I have not read the text carefully. I just skimmed it. But the color quality and selection of images merits two thumbs up, a gold star and a California sunset.

Stern and Chew bring together some better than average work into finance. If you have plowed through as many finance texts as I have during undergrad, graduate, and now post-grad work, you know how old this stuff can get. They have managed to bring in good information from a number of sources that didn't put me to sleep and actually taught me a thing or two when I had previously fooled myself into believeing I knew it all. Good book!

Readers looking to expand their understanding beyond the ideas in books like Valuation, Creating Shareholder Value, Quest For Value, etc should have this in the library. The collection provides great brain food for advanced corp fin ideas/debates for both securities analysts and corporations. The downside would be that application of some of the ideas presented would a bit difficult for an analyst (vs a company insider).