Population states and eigenstructures: A simplifying view of Markov learning models

DOI10.1016/0022-2496(80)90018-8zbMath0453.92020OpenAlexW2053501809MaRDI QIDQ1149398

Jill H. Larkin, Thomas D. Wickens

Publication date: 1980

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0022-2496(80)90018-8

zbMATH Keywords

transition operatorMarkov learning models

Mathematics Subject Classification ID

Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Eigenvalues, singular values, and eigenvectors (15A18) Stochastic matrices (15B51) Mathematical psychology (91E99)

Related Items (2)

How many parameters can a model have and still be testable? ⋮ Some deterministic models of concept identification

Cites Work

• Theory of learning with constant, variable, or contingent probabilities of reinforcement
• Presolution performance functions for Markov models
• Equivalence classes of functions of finite Markov chains
• Statistical methods for absorbing Markov-chain models for learning: Estimation and identification
• Parameterizations of Markov models for two-stage learning
• A comparison of paired-associate learning models having different acquisition and retention axioms
• Derivation of learning process statistics for a general Markov model
• 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
• Derivations of learning statistics from absorbing Markov chains
• An analysis of some conditions for representing \(N\) state Markov processes as general all or none models
• Matrix analysis of identifiability of some finite Markov models
• Application of a model to paired-associate learning
• Functions of Markov Chains
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