Efficient Bayesian parameter estimation in large discrete domains

Nir Friedman, Yoram Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 11 - Proceedings of the 1998 Conference, NIPS 1998
PublisherNeural information processing systems foundation
Pages417-423
Number of pages7
ISBN (Print)0262112450, 9780262112451
StatePublished - Jan 1 1999
Externally publishedYes
Event12th Annual Conference on Neural Information Processing Systems, NIPS 1998 - Denver, CO, United States
Duration: Nov 30 1998Dec 5 1998

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other12th Annual Conference on Neural Information Processing Systems, NIPS 1998
CountryUnited States
CityDenver, CO
Period11/30/9812/5/98

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Friedman, N., & Singer, Y. (1999). Efficient Bayesian parameter estimation in large discrete domains. In Advances in Neural Information Processing Systems 11 - Proceedings of the 1998 Conference, NIPS 1998 (pp. 417-423). (Advances in Neural Information Processing Systems). Neural information processing systems foundation.