Accession Number : ADA193628

Title :   Easy Bayes Estimation for Rasch-Type Models.

Descriptive Note : Technical rept. 15 Aug 86-30 Nov 87,

Corporate Author : SOUTH CAROLINA UNIV COLUMBIA CENTER FOR MACHINE INTELLIGENCE

Personal Author(s) : Jannarone, Robert J ; Laughlin, James E ; Yu, Kai F

PDF Url : ADA193628

Report Date : 04 Nov 1987

Pagination or Media Count : 15

Abstract : A Bayes estimation procedures is introduced that allows the nature and strength of prior beliefs to be easily specified and posterior models to be estimated with no more difficulty than maximum likelihood estimation. The procedure is based on constructing posterior distributions that are formally identical to likelihoods, but are constructed partly from sample data and partly from artificial data reflecting prior information. Improvements in performance of modal bayes procedures relative to maximum likelihood likelihood estimation procedures are illustrated for Rash-type models. Improvements range from modest to dramatic, depending on the model and the number of items being considered.

Descriptors :   *BAYES THEOREM, *MAXIMUM LIKELIHOOD ESTIMATION, MATHEMATICAL MODELS, MEASUREMENT

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE