This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called ‘prior ignorance’. The work is written from the authors’s committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critical re-examination of controversial issues. The level of mathematics used is such that most material is accessible to readers with knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. The book will be an ideal source for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics

Recent books in the Wiley Series in Probability and Mathematical Statistics Editors Vic Barnett J. Stuart Hunter Adrian F.M. Smith Geoffrey S. Watson Ralph A. Bradley Joseph B. Kadane Stephen M. Stigler Nicholas I. Fisher David G. Kendall Jozef L. Teugels Optimal Design of Experiments Friedrich Pukelsheim, Universität Augsburg, Augsburg, Germany Optimal Design of Experiments presents the first complete theoretical development of optimal design for the linear model, a unified exposition that embraces a wide variety of design problems. It describes the statistical theory involved in designing experiments, and applies it to typical special cases. The design problems originating from statistics are solved using tools from linear algebra and convex analysis. The material is presented in a very clear, careful and organized way. Rather than assaulting traditional ways of thinking about optimal design, this book pulls together formerly separate entities to create a common framework for diverse design problems that share a common goal. Statisticians, mathematicians, engineers, and operations research specialists will find this book stimulating, challenging, and an asset to their work. 1993 Statistics for Spatial Data, Revised Edition Noel Cressie, Iowa State University, USA Designed for the scientific and engineering professional eager to exploit its enormous potential, Statistics for Spatial Data is a primer to the theory as well as the nuts-and-bolts of this influential technique. Focusing on the three areas of geostatistical data, lattice data, and point patterns, the book sheds light on the link between data and model, and reveals how spatial statistical models can be used to solve a host of problems in science and engineering. The previous edition was hailed by Mathematical Reviews as "an excellent book which…will become a basic reference". Revised to reflect state-of-the-art developments, this edition also features many detailed examples, numerous illustrations, and over 1000 references. The first fully comprehensive introduction, Statistics for Spatial Data is an essential guide for professionals in biology, earth sciences, civil, electrical and agricultural engineering, geography, epidemiology, and ecology. 1993

The Valencia International Meetings on Bayesian Statistics, held every four years, provide the forum for researchers to come together and discuss frontier developments in the field. The resulting Proceedings provide a definitive, up-to-date overview encompassing a wide range of theoretical and applied research. This fifth Proceedings is no exception. In particular, it reflects a growing emphasis on computational issues, concerned with making Bayesian methods routinely available to applied practitioners, both statisticians and speciailists in other subject-matter, whose work depends on careful quantification of uncertainties. This book contains several invited papers by leading authorities.

The Valencia International Meetings on Bayesian Statistics, held every four years, provide the forum for researchers to come together to present and discuss frontier developments in the field. The resulting proceedings provide a definitive, up-to-date overview encompassing a wide range of theoretical and applied research. This fourth volume of proceedings is no exception. In particular, it reflects a growing emphasis on computational issues, concerned with making Bayesian methods routinely available to applied practitioners, both statisticians and other specialists whose work depends on careful quantification of uncertainties. The growing interest in Bayesian methods is revealed by the ever-increasing participation in the Valencia meetings. This, in turn, is reflected in the high quality of this work, which contains 30 invited papers by leading authorities and 33 refereed contributed papers, selected from over 150 presented at the meeting.

The Valencia International Meetings on Bayesian Statistics, held every four years, provide the main forum for researchers in the area of Bayesian Statistics to come together to present and discuss frontier developments in the field. Covering a broad range of applications and models, including genetics, computer vision and computation, the resulting proceedings provide a definitive, up-to-date overview encompassing a wide range of theoretical and applied research. This eighth proceedings includes edited and refereed versions of 20 invited papers plus extensive and in-depth discussion along with 19 extended four page abstracts of the best presentations offering a wide perspective of the developments in Bayesian statistics over the last four years.