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An Analysis of Some Conditions for Representing N State Markov Processes as General all or None Models
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf02289599openalex-snapshot
Markovian Processes with Identifiable States: General Considerations and Application to All-or-None Learning
inferredcites →https://doi.org/10.1007/bf02290151openalex-snapshot
Comments on “Markovian Processes with Identifiable States: General Considerations and Applications to All-or-None Learning”
inferredcites →https://doi.org/10.1007/bf02290152openalex-snapshot
Identifiability and Statistical Properties of Two-Stage Learning with no Successes in the Initial Stage
inferredcites →https://doi.org/10.1016/0022-2496(64)90005-7openalex-snapshot
A comparison of paired-associate learning models having different acquisition and retention axioms
inferredcites →https://doi.org/10.1016/0022-2496(65)90020-9openalex-snapshot
A forgetting model for paired-associate learning
inferredcites →https://doi.org/10.1016/0022-2496(67)90033-8openalex-snapshot
Paired-associate learning with short-term retention: Mathematical analysis and data regarding identification of parameters
inferredcites →https://doi.org/10.1037/h0027248openalex-snapshot
Nonstationary performance before all-or-none learning.
inferredcites →https://doi.org/10.1037/h0040894openalex-snapshot
A fundamental property of all-or-none models, binomial distribution of responses prior to conditioning, with application to concept formation in children.
inferredcites →https://doi.org/10.1214/aoms/1177706444openalex-snapshot
A Markovian Function of a Markov Chain
inferredcites →https://doi.org/10.2307/2309886openalex-snapshot
Introduction to Difference Equations.
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/bf02291365openalex-snapshot
Matrix Analysis of Identifiability of Some Finite Markov Models
inferred← citeshttps://doi.org/10.1007/bf02293556openalex-snapshot
Precriterion Stationarity in Markovian Learning Models
inferred← citeshttps://doi.org/10.1007/bf02294000openalex-snapshot
Models, Computers and Policies: Fifty Years of Psychometrika
inferred← citeshttps://doi.org/10.1016/0022-2496(74)90029-7openalex-snapshot
Equivalence classes of functions of finite Markov chains
inferred← citeshttps://doi.org/10.1016/0022-2496(80)90018-8openalex-snapshot
Population states and eigenstructure: A simplifying view of Markov learning models
inferred← citeshttps://doi.org/10.1063/5.0198886openalex-snapshot
Hidden Markov model to provide coordination relationship for learning behavior development in the era of industrial reform
inferred← citeshttps://doi.org/10.1111/j.1467-8624.1980.tb02554.xopenalex-snapshot
Developmental Invariance in a Mathematical Model of Associative Learning
inferred← citeshttps://doi.org/10.1146/annurev.ps.24.020173.000501openalex-snapshot
Mathematical Learning Theory and the New "Mental Forestry"
inferred← citeshttps://doi.org/10.20982/tqmp.07.2.p032openalex-snapshot
Hidden Markov models and learning in authentic situations
inferred← citeshttps://doi.org/10.61707/52exbt60openalex-snapshot
Hidden Markov Model to Optimize Coordination Relationship for Learning Behaviour
inferred← citeshttps://doi.org/10.7202/1024958aropenalex-snapshot
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