Universal Models for the Exponential Distribution

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) cod...

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Bibliographic Details
Published in:IEEE transactions on information theory 2009-07, Vol.55 (7), p.3087-3090
Main Authors: Schmidt, D.F., Makalic, E.
Format: Article
Language:English
Subjects:
Applied sciences
Australia
Bayesian analysis
Bayesian methods
Bulk molding compounds
Codes
Construction
Distributed computing
Exact sciences and technology
Exponential distribution
Information
Information theory
Information, signal and communications theory
Integral equations
Maximum likelihood estimation
Maximum likelihood method
Messages
Minimax techniques
Minimum description length (MDL)
minimum message length (MML)
Parametric statistics
Predictive models
Statistical distributions
Telecommunications and information theory
Theory
universal models
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Summary: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.
ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2009.2018331