Statistical Decision Theory and Bayesian Analysis

Author: Berger
Publisher:
Publish Date: 1998-05-01
Features: This book requires a very low level of statistical knowledge from the reader in terms of formality, and it is not necessary to have prior knowledge of Bayesian analysis, decision theory, or advanced statistics. However, for those who have not even taken intermediate-level statistics courses, reading this book may be quite challenging. The book primarily discusses the basic principles and arguments of Bayesian analysis and decision theory, and does not intend to systematically introduce the full scope of methods that have already been developed. This means it does not cover the specific developments of these ideas in special fields of statistics. The examples in the book are largely arbitrary, and therefore, unfortunately, they do not cover some of the more difficult parts of statistics, such as nonparametric statistics. However, a considerable amount of methodological content is ultimately introduced through one means or another. The book primarily discusses the basic principles and arguments of Bayesian analysis and decision theory, and does not intend to systematically introduce the full scope of methods that have already been developed. This means it does not cover the specific developments of these ideas in special fields of statistics. The examples in the book are largely arbitrary, and therefore, unfortunately, they do not cover some of the more difficult parts of statistics, such as nonparametric statistics. However, a considerable amount of methodological content is ultimately introduced through one means or another. The book requires a very low level of statistical knowledge from the reader in terms of formality, and it is not necessary to have prior knowledge of Bayesian analysis, decision theory, or advanced statistics. However, for those who have not even taken intermediate-level statistics courses, reading this book may be quite challenging.