The Estimation Of Probabilities: An Essay on Modern Bayesian Methods

by Irving John Good

The problem of how to estimate probabilities has interested philosophers, statisticians, actuaries, and mathematicians for a long time. It is currently of interest for automatic recognition, medical diagnosis, and artificial intelligence in general. This monograph reviews existing methods, including those that are new or have not been written up in a connected manner. The problem of how to estimate probabilities has interested philosophers, statisticians, actuaries, and mathematicians for a long time. It is currently of interest for automatic recognition, medical diagnosis, and artificial intelligence in general. The main purpose of this monograph is to review existing methods, especially those that are new or have not been written about in an organized way. The need for nontrivial theory arises because our samples are usually too small for us to rely exclusively on the frequency definition of probability. Most of the techniques described in this book depend on a modern Bayesian approach. The maximum-entropy principle, also relevant to this discussion, is used in the last chapter. It is hoped that the book will stimulate further work in a field whose importance will increasingly be recognized. Methods for estimating probabilities are related to another part of statistics, namely, significance testing, and example of this relationship are also presented. Many readers will be persuaded by this work that it is necessary to make use of a theory of subjective probability in order to estimate physical probabilities and also that a useful idea is that of a hierarchy of three types of probability which can sometimes be identified with physical, logical, and subjective probabilities.

109 pages, Paperback

First published March 17, 2003

Reviews

[Andrew Dalby · Rating 4 out of 5]

A very significant view on the interpretation of probability and its estimation. This is a Bayesian perspective and it is written at a high level of mathematics. At times it is very dense but the key concepts come through.