@inproceedings{2acd092ff5994faa81d9e4496412d131,
title = "Efficient Bayesian parameter estimation in large discrete domains",
abstract = "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.",
author = "Nir Friedman and Yoram Singer",
year = "1999",
language = "English (US)",
isbn = "0262112450",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
pages = "417--423",
booktitle = "Advances in Neural Information Processing Systems 11 - Proceedings of the 1998 Conference, NIPS 1998",
note = "12th Annual Conference on Neural Information Processing Systems, NIPS 1998 ; Conference date: 30-11-1998 Through 05-12-1998",
}