Hypothesis Testing with Incompletely Known Distribution Function of the Observations and Unknown A Priori Probabilities of the Hypotheses

An optimal decision rule is derived for testing two hypotheses under incompletely known distributions of the observed signals and unknown a priori probabilities of the hypotheses. Optimality is understood in the sense of minimizing the maximum excess of average risk over Bayesian risk. Some examples are considered.