Stochastic Models for Learning

Robert R. Bush

ISBN 10: 1614273197 / ISBN 13: 9781614273196

Published by Martino Fine Books, 2012

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 2012 Reprint of 1955 Edition. Exact facsimile of the original edition, not reproduced with Optical Recognition Software. A stochastic process is one in which the probabilities of a set of events keep changing with time. Bush and Mosteller make use of the mathematical techniques developed for the study of such processes in building a theory of learning and then apply the theory to explain the results of several learning experiments. Contents: Part I: The mathematical system and the general model -- 1. The basic model -- 2. Stimulus sampling and conditioning -- 3. Sequences of events -- 4. Distributions of response probabilities -- 5. The equal alpha condition -- 6. Approximate methods -- 7. Operators with limits zero and unity -- 8. Commuting operators -- Part II: Applications -- 9. Identification and estimation -- 10. Free-recall verbal learning -- 11. Avoidance training -- 12. An experiment on imitation -- 13. Symmetric choice problems -- 14. Runway experiments -- 15. Evaluations.

Seller Inventory # 9781614273196

Bibliographic Details

• Title: Stochastic Models for Learning
• Publisher: Martino Fine Books
• Publication Date: 2012
• Language: English
• ISBN 10: 1614273197
• ISBN 13: 9781614273196
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 23D millimeters 229 millimeters width by 152 millimeters height by 23 millimeters depth
• Weight: 619 grams

About this title

Synopsis

2012 Reprint of 1955 Edition. Exact facsimile of the original edition, not reproduced with Optical Recognition Software. A stochastic process is one in which the probabilities of a set of events keep changing with time. Bush and Mosteller make use of the mathematical techniques developed for the study of such processes in building a theory of learning and then apply the theory to explain the results of several learning experiments. Contents: Part I: The mathematical system and the general model -- 1. The basic model -- 2. Stimulus sampling and conditioning -- 3. Sequences of events -- 4. Distributions of response probabilities -- 5. The equal alpha condition -- 6. Approximate methods -- 7. Operators with limits zero and unity -- 8. Commuting operators -- Part II: Applications -- 9. Identification and estimation -- 10. Free-recall verbal learning -- 11. Avoidance training -- 12. An experiment on imitation -- 13. Symmetric choice problems -- 14. Runway experiments -- 15. Evaluations.