Classes of lower bounds on outage error probability and MSE in Bayesian parameter estimation

In this paper, new classes of lower bounds on the outage error probability and on the mean-square-error (MSE) in Bayesian parameter estimation are proposed. The minima of the h-outage error probability and the MSE are obtained by the generalized maximum a-posteriori probability and the minimum MSE (MMSE) estimators, respectively. However, computation of these estimators and their corresponding performance is usually not tractable and thus, lower bounds on these terms can be very useful for performance analysis. The proposed class of lower bounds on the outage error probability is derived using Hölder’s inequality. This class is utilized to derive a new class of Bayesian MSE bounds. It is shown that for unimodal symmetric conditional probability density functions (pdf) the tightest probability of outage error lower bound in the proposed class attains the minimum probability of outage error and the tightest MSE bound coincides with the MMSE performance. In addition, it is shown that the proposed MSE bounds are always tighter than the Ziv-Zakai lower bound (ZZLB). The proposed bounds are compared with other existing performance lower bounds via some examples.