Efficient Bayesian Parameter Estimation in Large Discrete Domains
We examine the problem of estimating the parameters of a multinomial distribution over a large number of discrete outcomes, most of which do not appear in the training data. We analyze this problem from a Bayesian perspective and develop a hierarchical prior that incorporates the assumption that the observed outcomes constitute only a small subset of the possible outcomes. We show how to efficiently perform exact inference with this form of hierarchical prior and compare it to standard approaches.