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Learning in Models with Fixed Structure

  • Russell G. Almond
  • Robert J. Mislevy
  • Linda S. Steinberg
  • Duanli Yan
  • David M. Williamson
Chapter
Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Abstract

The preceding chapters have described an approach to assessment design and analysis that exploits the advantages of Bayesian networks. This chapter addresses the problem of estimating these distributions or parameters they are modeled in terms of. It lays out a general Bayesian framework for expressing educational measurement models in these terms. It then describes and illustrates two estimation approaches: Bayes modal estimation via the expectation–maximization (EM) algorithm and Markov Chain Monte Carlo (MCMC) estimation.

Keywords

Markov Chain Monte Carlo Differential Item Functioning Gibbs Sampling Proposal Distribution Conditional Probability Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Russell G. Almond
    • 1
  • Robert J. Mislevy
    • 2
  • Linda S. Steinberg
    • 3
  • Duanli Yan
    • 2
  • David M. Williamson
    • 2
  1. 1.Florida State UniversityTallahasseeUSA
  2. 2.Educational Testing ServicePrincetonUSA
  3. 3.PenningtonUSA

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