Universal models for the exponential distribution

Authors:
Daniel F. Schmidt;Enes Makalic
Affiliations:
Centre for MEGA Epidemiology, University of Melbourne, Carlton, VIC, Australia;Centre for MEGA Epidemiology, University of Melbourne, Carlton, VIC, Australia
Venue:
IEEE Transactions on Information Theory
Year:
2009

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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.