Optimal Rate-Exponent Region for a Class of Hypothesis Testing Against Conditional Independence Problems

Abstract

We study a class of distributed hypothesis testing against conditional independence problems. Under the criterion that stipulates minimization of the Type II error rate subject to a (constant) upper bound $\epsilon$ on the Type I error rate, we characterize the set of encoding rates and exponent for both discrete memoryless and memoryless vector Gaussian settings.

Submitted for publication to the IEEE Information Theory Workshop, ITW 2019. arXiv admin note: substantial text overlap with arXiv:1811.03933