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Estimation in the Three-State Markov Learning Model

Published online by Cambridge University Press:  01 January 2025

Helena Chmura Kraemer
Helena Chmura Kraemer*
Affiliation:
Stanford University
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Abstract

The problem of estimation of the parameters of the Bower three-state learning model is discussed for three forms of the model. In the simplest case, a minimum variance unbiased estimator is found and is presented with its asymptotic distribution theory and with a method of obtaining approximate confidence intervals. In the other cases, methods are discussed for obtaining the maximum likelihood estimators at least approximately. All estimation techniques are illustrated by application to a set of data obtained by Theios.

Type
Original Paper
Information
Psychometrika , Volume 29 , Issue 2 , June 1964 , pp. 131 - 141
Copyright
Copyright © 1964 Psychometric Society

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Footnotes

*

This research was supported by the Office of Naval Research Contract Nonr-225(17), (NR 171-034).

References

Bower, G. H. General three-state Markov learning models, Stanford, California: Institute for Mathematical Studies in the Social Sciences, Stanford University, 1961.Google Scholar
Theios, J. A three-state Markov model for learning, Stanford, California: Institute for Mathematical Studies in the Social Sciences, Stanford University, 1961.Google Scholar