General Classes of Lower Bounds on the Probability of Error in Multiple Hypothesis Testing

In this paper, two new classes of lower bounds on the probability of error for m-ary hypothesis testing are proposed. Computation of the minimum probability of error which is attained by the maximum a-posteriori probability (MAP) criterion, is usually not tractable. The new classes are derived using Hölder’s inequality and reverse Hölder’s inequality. The bounds in these classes provide good prediction of the minimum probability of error in multiple hypothesis testing. The new classes generalize and extend existing bounds and their relation to some existing upper bounds is presented. It is shown that the tightest bounds in these classes asymptotically coincide with the optimum probability of error provided by the MAP criterion for binary or multiple hypothesis testing problem. These bounds are compared with other existing lower bounds in several typical detection and classification problems in terms of tightness and computational complexity.