By Topic

Hypothesis testing and information theory

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)

The testing of binary hypotheses is developed from an information-theoretic point of view, and the asymptotic performance of optimum hypothesis testers is developed in exact analogy to the asymptotic performance of optimum channel codes. The discrimination, introduced by Kullback, is developed in a role analogous to that of mutual information in channel coding theory. Based on the discrimination, an error-exponent functione(r)is defined. This function is found to describe the behavior of optimum hypothesis testers asymptotically with block length. Next, mutual information is introduced as a minimum of a set of discriminations. This approach has later coding significance. The channel reliability-rate functionE(R)is defined in terms of discrimination, and a number of its mathematical properties developed. Sphere-packing-like bounds are developed in a relatively straightforward and intuitive manner by relatinge(r)andE (R). This ties together the aforementioned developments and gives a lower bound in terms of a hypothesis testing model. The result is valid for discrete or continuous probability distributions. The discrimination function is also used to define a source code reliability-rate function. This function allows a simpler proof of the source coding theorem and also bounds the code performance as a function of block length, thereby providing the source coding analog ofE (R).

Published in:

Information Theory, IEEE Transactions on  (Volume:20 ,  Issue: 4 )