, Volume 55, Issue 3, pp 449–460 | Cite as

Easy bayes estimation for rasch-type models

  • Robert J. Jannarone
  • Kai F. Yu
  • James E. Laughlin


A Bayes estimation procedure is introduced that allows the nature and strength of prior beliefs to be easily specified and modal posterior estimates to be obtained as easily as maximum likelihood estimates. The procedure is based on constructing posterior distributions that are formally identical to likelihoods, but are based on sampled data as well as artificial data reflecting prior information. Improvements in performance of modal Bayes procedures relative to maximum likelihood estimation are illustrated for Rasch-type models. Improvements range from modest to dramatic, depending on the model and the number of items being considered.

Key words

Rasch model conjugate Bayes estimation artificial data augmentation conjunctive Markov models machine learning neural learning models 


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Copyright information

© The Psychometric Society 1990

Authors and Affiliations

  • Robert J. Jannarone
    • 1
  • Kai F. Yu
    • 1
  • James E. Laughlin
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of South CarolinaColumbia

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