Abstract:
We consider the problem of modeling a distribution whose alphabet size is large relative to the amount of observed data. It is well known that conventional maximum-likeli...Show MoreMetadata
Abstract:
We consider the problem of modeling a distribution whose alphabet size is large relative to the amount of observed data. It is well known that conventional maximum-likelihood estimates do not perform well in that regime. Instead, we find the distribution maximizing the probability of the data's pattern. We derive an efficient algorithm for approximating this distribution. Simulations show that the computed distribution models the data well and yields general estimators that evaluate various data attributes as well as specific estimators designed especially for these tasks
Date of Conference: 27 June 2004 - 02 July 2004
Date Added to IEEE Xplore: 10 January 2005
Print ISBN:0-7803-8280-3