On the Subexponential Decay of Detection Error Probabilities in Long Tandems
Wee Peng Tay, John N. Tsitsiklis, Moe Z. Win · IEEE Transactions on Information Theory · 2008
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 alld> 1/2, the error probability is Omega(e-cnd), wherecis a positive constant. Furthermore, the bound Omega(e-c(logn)d) , for alld> 1, holds under an additional mild condition on the distributions. This latter bound is shown to be tight.