Easy Bayes Estimation for Rasch-Type Models

摘要

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.