Efficient Bayesian Parameter Estimation in Large Discrete Domains

In this paper we examine the problem of estimating the parameters of a multinomial distribution over a large number of discreteoutcomes,most of which do not appearin 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 our method to standard approaches and demonstrate its merits. Category: Algorithms and Architectures Presentation preference: none This paper was not submitted elsewhere nor will be submitted during NIPS review period.

• Type: Conference
• Year: 1998
• Where: NIPS
• Authors: Nir Friedman, Yoram Singer
• Keywords: Architectures Presentation Preference, Hierarchical Prior, NIPS 1998, NIPS 2007, Observed Outcomes