Bayesian Networks in Educational Assessment pp 279-330 | Cite as

# Learning in Models with Fixed Structure

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## 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