Universal Models for the Exponential Distribution

2009/06/16 by Daniel F. Schmidt, Enes Makalic · 2 citations
Computer Science · #Bayesian Methods and Mixture Models #Error Correcting Code Techniques #Algorithms and Data Compression

paper · doi:10.1109/tit.2009.2018331

openalex publication_date 2009/06/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

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 withinO(1) of the MML codelength, with suitable data driven priors.

Citations

• On Information and Sufficiency

Cited by

• Realtime price impact detection
• Exponential distribution [wikipedia]

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