On the Subexponential Decay of Detection Error Probabilities in Long Tandems

We consider the problem of Bayesian decentralized binary hypothesis testing in a network of sensors arranged in a tandem. We show that the rate of error probability decay is always subexponential, establishing the validity of a long-standing conjecture. Under the additional assumption of bounded Kullback-Leibler (KL) divergences, we show that for all d > 1/2, the error probability is Omega(e - c nd), where c is a positive constant. Furthermore, the bound Omega(e - c (logn)d) , for all d > 1, holds under an additional mild condition on the distributions. This latter bound is shown to be tight.

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