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An Analysis of Some Conditions for Representing N State Markov Processes as General all or None Models

Published online by Cambridge University Press:  01 January 2025

Theodore E. Steiner  and
James G. Greeno
Theodore E. Steiner
Affiliation:
University of Illinois
James G. Greeno
Affiliation:
University of Michigan
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Abstract

Recently Markov learning models with two unidentifiable presolution success states, an error state, and an absorbing learned state, have been suggested to handle certain aspects of data better than the three state Markov models of the General All or None model type. In attempting to interpret psychologically, and evaluate statistically the adequacy of various classes of Markov models, a knowledge of the relationship between the classes of models would be helpful. This paper considers some aspects of the relationship between the class of General All or None models and the class of Stationary Absorbing Markov models with N error states, and M presolution success states.

Type
Original Paper
Information
Psychometrika , Volume 34 , Issue 4 , December 1969 , pp. 461 - 487
Copyright
Copyright © 1969 The Psychometric Society

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