Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TIT.2009.2018331 | IEEE Transactions on Information Theory |
Keywords | Field | DocType |
codes,exponential distribution,Bayes mixture code,conditional normalized maximum likelihood code,exponential distribution,information theoretic universal models,minimum message length code,sequential normalized maximum likelihood code,Minimum description length (MDL),minimum message length (MML),universal models | Information theory,Data modeling,Discrete mathematics,Minimum message length,Algorithm,Exponential distribution,Statistics,Prior probability,Universal code,Mathematics,Bayesian probability,Bayes' theorem | Journal |
Volume | Issue | ISSN |
55 | 7 | 0018-9448 |
Citations | PageRank | References |
2 | 0.37 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel F. Schmidt | 1 | 51 | 10.68 |
| Enes Makalic | 2 | 55 | 11.54 |