Sharp bounds on aggregate expert error

We revisit the classic problem of aggregating binary advice from conditionally independent experts, also known as the Naive Bayes setting. Our quantity of interest is the error probability of the optimal decision rule. In the case of symmetric errors (sensitivity = specificity), reasonably tight bounds on the optimal error probability are known. In the general asymmetric case, we are not aware of any nontrivial estimates on this quantity. Our contribution consists of sharp upper and lower bounds on the optimal error probability in the general case, which recover and sharpen the best known results in the symmetric special case. Additionally, our bounds are apparently the first to take the bias into account. Since this turns out to be closely connected to bounding the total variation distance between two product distributions, our results also have bearing on this important and challenging problem.