Hostname: page-component-6bf8c574d5-gr6zb Total loading time: 0 Render date: 2025-02-27T02:19:45.011Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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