Bayes Decision Rules Based on Objective Priors

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.