Bounding Means of Discrete Distributions.

We introduce methods to bound the mean of a discrete distribution (or finite population) based on sample data, for random variables with a known set of possible values. In particular, the methods can be applied to categorical data with known category-based values. For small sample sizes, we show how to leverage knowledge of the set of possible values to compute bounds that are stronger than for general random variables such as standard concentration inequalities.