Open Access
June, 1983 A Universal Prior for Integers and Estimation by Minimum Description Length
Jorma Rissanen
Ann. Statist. 11(2): 416-431 (June, 1983). DOI: 10.1214/aos/1176346150


An earlier introduced estimation principle, which calls for minimization of the number of bits required to write down the observed data, has been reformulated to extend the classical maximum likelihood principle. The principle permits estimation of the number of the parameters in statistical models in addition to their values and even of the way the parameters appear in the models; i.e., of the model structures. The principle rests on a new way to interpret and construct a universal prior distribution for the integers, which makes sense even when the parameter is an individual object. Truncated real-valued parameters are converted to integers by dividing them by their precision, and their prior is determined from the universal prior for the integers by optimizing the precision.


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Jorma Rissanen. "A Universal Prior for Integers and Estimation by Minimum Description Length." Ann. Statist. 11 (2) 416 - 431, June, 1983.


Published: June, 1983
First available in Project Euclid: 12 April 2007

zbMATH: 0513.62005
MathSciNet: MR696056
Digital Object Identifier: 10.1214/aos/1176346150

Primary: 62A99
Secondary: 62F10

Keywords: likelihood , modeling , parameters

Rights: Copyright © 1983 Institute of Mathematical Statistics

Vol.11 • No. 2 • June, 1983
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