  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.
