Minimum description complexity

The classical problem of model selection among parametric model sets is considered. The goal is to choose a model set which best represents observed data. The critical task is the choice of a criterion for model set comparison. Pioneer information theoretic based approaches to this problem are Akaike information criterion (AIC) and different forms of minimum description length (MDL). The prior assumption in these methods is that the unknown true model is a member of all the competing sets. We introduce a new method of model selection: minimum description complexity (MDC). The approach is motivated by the Kullback-Leibler information distance. The method suggests choosing the model set for which the model set relative entropy is minimum. We provide a probabilistic method of MDC estimation for a class of parametric model sets. In this calculation the key factor is our prior assumption: unlike the existing methods, no assumption of the true model being a member of the competing model sets is needed. The main strength of the MDC calculation is in its method of extracting information from the observed data.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2002

Author dc:creator

• Beheshti, Soosan, 1969-

Advisor dc:contributor.advisor

• Munther A. Dahleh.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/8012

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/8012