Seymour Geisser is a well known statistician who has made many significant contributions to the field of statistics. I met and got to know Seymour when I was a graduate student at Stanford University. He was a visiting professor there one year. We both participated in a seminar on rereading the works of R. A. Fisher. I presented a review Fisher's controversial work on the Behrens-Fisher problem and fiducial inference along with the insightful and critical comments of Maurice Bartlett. Seymour was one of the most knowledgeable members of the class and he contributed much to the discussion of several reviews in the class, especially mine.

A Conference took place in 1994 in Taiwan in celebration of Seymour's 65th birthday. This volume contains the proceedings from that conference. It was published in 1996. It contains papers by many distinguished statisticians in areas of interest to Seymour including general methodology, prediction theory, design of experiments and classification, Bayesian inference, testing and model selection, modelling and prediction in finance and time series modeling.

Seymour was a leader in the development of the Department of Statistics at the University of Minnesota and personal reminiscences are given by Marvin Zelen and David Lane about Seymour during his terms at SUNY at Buffalo and at the University of Minnesota. Seymour is well known for his work in Bayesian prediction, DNA forensic science and sample reuse. His approach to statistical inference is summarized in his book on predictive inference.

These proceedings include 27 papers presented at that conference. The editors, Jack Lee, Wesley Johnson and Arnold Zellner all contributed interesting articles. Many other prominent Bayesian statisticians also contributed articles including Leonard and Hsu, Press, Geweke, Tiao, Berger, McCulloch, Parmigiani and Zelen.

This volume gives you a perspective on Seymour Geisser as a man and an influential statistician and presents an interesting body of research that will interest many statistical researchers, especially Bayesians.