TY - GEN
T1 - Efficient Bayesian parameter estimation in large discrete domains
AU - Friedman, Nir
AU - Singer, Yoram
PY - 1999
Y1 - 1999
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84893179806&partnerID=8YFLogxK
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:84893179806
SN - 0262112450
SN - 9780262112451
T3 - Advances in Neural Information Processing Systems
SP - 417
EP - 423
BT - Advances in Neural Information Processing Systems 11 - Proceedings of the 1998 Conference, NIPS 1998
PB - Neural information processing systems foundation
T2 - 12th Annual Conference on Neural Information Processing Systems, NIPS 1998
Y2 - 30 November 1998 through 5 December 1998
ER -