Journal article

Universal Models for the Exponential Distribution

Daniel F Schmidt, Enes Makalic

IEEE TRANSACTIONS ON INFORMATION THEORY | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Published : 2009

Abstract

This paper considers the problem of constructing information theoretic universal models for data distributed according to the exponential distribution. The universal models examined include the sequential Normalized Maximum Likelihood (SNML) code, conditional normalized maximum likelihood (CNML) code, the minimum message length (MML) code, and the Bayes mixture code (BMC). The CNML code yields a codelength identical to the Bayesian mixture code, and within O(1) of the MML codelength, with suitable data driven priors. © 2009 IEEE.