Model Selection by Normalized Maximum Likelihood

Model Selection by Normalized Maximum Likelihood (2005)

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by Jay I. Myung , Daniel J. Navarro , Mark A. Pitt

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Datum	Value	Source
TITLE	Model Selection by Normalized Maximum Likelihood	SVM HeaderParse 0.2
AUTHOR NAME	Jay I. Myung	user correction
AUTHOR AFFIL	Department of Psychology; Ohio State University	user correction
AUTHOR ADDR	238 Townshend Hall; 1885 Neil Avenue Mall; Columbus, Ohio 43210-1222	user correction
AUTHOR NAME	Daniel J. Navarro	user correction
AUTHOR AFFIL		user correction
AUTHOR ADDR		user correction
AUTHOR NAME	Mark A. Pitt	user correction
AUTHOR AFFIL	Department of Psychology; Ohio State University	user correction
AUTHOR ADDR	238 Townshend Hall; 1885 Neil Avenue Mall; Columbus, Ohio 43210-1222	user correction
ABSTRACT	The Minimum Description Length (MDL) principle is an information theoretic approach to inductive inference that originated in algorithmic coding theory. In this approach, data are viewed as codes to be compressed by the model. From this perspective, models are compared on their ability to compress a data set by extracting useful information in the data apart from random noise. The goal of model selection is to identify the model, from a set of candidate models, that permits the shortest description length (code) of the data. Since Rissanen originally formalized the problem using the crude ‘two-part code ’ MDL method in the 1970s, many significant strides have been made, especially in the 1990s, with the culmination of the development of the refined ‘universal code’ MDL method, dubbed Normalized Maximum Likelihood (NML). It represents an elegant solution to the model selection problem. The present paper provides a tutorial review on these latest developments with a special focus on NML. An application example of NML in cognitive modeling is also provided.	user correction
YEAR	2005	user correction
CITATIONS	57 found	ParsCit 1.0