An analysis of some conditions for representing \(N\) state Markov processes as general all or none models

DOI10.1007/BF02290602zbMath0211.23402OpenAlexW1967856252MaRDI QIDQ2544134

T. E. Steiner, James G. Greeno

Publication date: 1969

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02290602

Mathematics Subject Classification ID

Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Memory and learning in psychology (91E40)

Related Items

Population states and eigenstructures: A simplifying view of Markov learning models ⋮ Equivalence classes of functions of finite Markov chains ⋮ Matrix analysis of identifiability of some finite Markov models

Cites Work

• A comparison of paired-associate learning models having different acquisition and retention axioms
• Markovian processes with identifiable states: General considerations and application to all-or-none learning
• Paired-associate learning with short-term retention: Mathematical analysis and data regarding identification of parameters
• A Markovian Function of a Markov Chain
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