Mathematical Learning Models — Theory and Algorithms: Proceedings of a Conference (Lecture Notes in Statistics)

ISBN 10: 0387909133 / ISBN 13: 9780387909134

Editore: Springer, 1983

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Dati bibliografici

• Titolo: Mathematical Learning Models ? Theory and Algorithms: Proceedings of a Conference (Lecture Notes in Statistics)
• Editore: Springer
• Data di pubblicazione: 1983
• Lingua: Inglese
• ISBN 10: 0387909133
• ISBN 13: 9780387909134
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

This volume contains most of the contributions presented at the conference "Mathematical Learning Models - Theory and Algorithms". The main subjects of the program were: - mathematical learning models, - bandit problems, - stochastic approximation procedures, - sequential decision processes with unknown law of nature.

Contenuti

The Minimax Risk for the Two-Armed Bandit Problem.- Bandit Problems with Random Discounting.- Stochastic Approximation on a Bounded Convex Set.- Learning Automaton for Finite Semi-Markov Decision Processes.- A Local Asymptotic Minimax Optimality of an Adaptive Robbins-Monro Stochastic Approximation Procedure.- Dynamic Allocation Indices for Bayesian Bandits.- The Role of Dynamic Allocation Indices in the Evaluation of Suboptimal Strategies for Families of Bandit Processes.- On the Discretization Technique for Optimal Discounted Control of the Wiener Process.- Asymptotic Properties of Learning Models.- On the Infinitesimal Characterization of Monotone Stopping Problems in Continuous Time.- Numerical Investigation of the Two-Armed Bandit.- Uniform Bounds for a Dynamic Programming Model under Adaptive Control Using Exponentially Bounded Error Probabilities.- Stochastic Regression Models and Consistency of the Least Squares Identification Scheme.- Recursive Identification Techniques.- An Optimization Problem for Matrices with Application to Decision Models.- On a Class of Learning Algorithms with Symmetric Behavior under Success and Failure.- Convergence of a General Stochastic Approximation Process under Convex Constraints and Some Applications.- On Kersting’s Theorem on Weak Convergence of Recursions.- On Continuous Time Learning Models.- Convergence of Stochastic Approximation Algorithms with Non-Additive Dependent Disturbances and Applications.- Sequential Probability Ratio Tests for Homogeneous Markov Chains.- Allocation Rules for Sequential Clinical Trials.- Non-Deterministic Modelling and its Application in Adaptive Optimal Control.