By Topic

Bayes Decision Rules Based on Objective Priors

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

2 Author(s)

The problem of statistical decision making under uncertainty is considered. A Bayes approach based upon prior probabilities which are found using an objective inference technique developed by Kashyap is proposed as the basic solution procedure. The problem is formulated in a statistical decision theory format and the general solution technique is outlined. Using this inference technique, it is possible to have different priors for different experiments. A general decision criterion is formulated to handle these situations. It is shown that in situations where the experimentation is fixed and the decision problem is faced repeatedly, but not necessarily an infinite number of times, this approach is justifiable. In situations where there is a choice of experiments, these arguments are not as conclusive; however, the approach still has practical merit as an objective alternative to the minimax approach.

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:SMC-3 ,  Issue: 4 )