Mathematical Learning Models — Theory and Algorithms

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.