Universal Models for the Exponential Distribution
Daniel F. Schmidt, Enes Makalic · IEEE Transactions on Information Theory · 2009
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 withinO(1) of the MML codelength, with suitable data driven priors.