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System identification --- Linear models (Statistics) --- Learning models (Stochastic processes) --- Systèmes, Identification des --- Modèles linéaires (statistique) --- Modèles stochastiques d'apprentissage --- Congresses. --- Congrès --- #TELE:SISTA --- Systèmes, Identification des --- Modèles linéaires (statistique) --- Modèles stochastiques d'apprentissage --- Congrès --- Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- Statistics --- Mathematical learning models (Stochastic processes) --- Models, Learning (Stochastic processes) --- Stochastic learning models (Stochastic processes) --- Learning --- Sequential analysis --- Stochastic processes --- Congresses

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Stochastic learning and optimization is a multidisciplinary subject that has wide applications in modern engineering, social, and financial problems, including those in Internet and wireless communications, manufacturing, robotics, logistics, biomedical systems, and investment science. This book is unique in the following aspects. (Four areas in one book) This book covers various disciplines in learning and optimization, including perturbation analysis (PA) of discrete-event dynamic systems, Markov decision processes (MDP)s), reinforcement learning (RL), and adaptive control, within a unified framework. (A simple approach to MDPs) This book introduces MDP theory through a simple approach based on performance difference formulas. This approach leads to results for the n-bias optimality with long-run average-cost criteria and Blackwell's optimality without discounting. (Event-based optimization) This book introduces the recently developed event-based optimization approach, which opens up a research direction in overcoming or alleviating the difficulties due to the curse of dimensionality issue by utilizing the system's special features. (Sample-path construction) This book emphasizes physical interpretations based on the sample-path construction.

Learning models (Stochastic processes) --- Mathematical optimization. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Mathematical learning models (Stochastic processes) --- Models, Learning (Stochastic processes) --- Stochastic learning models (Stochastic processes) --- Learning --- Sequential analysis --- Stochastic processes --- Mathematical models --- Computational complexity. --- Engineering design. --- Artificial intelligence. --- Distribution (Probability theory. --- Discrete Mathematics in Computer Science. --- Engineering Design. --- Control and Systems Theory. --- Artificial Intelligence. --- Calculus of Variations and Optimal Control; Optimization. --- Probability Theory and Stochastic Processes. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Fifth generation computers --- Neural computers --- Design, Engineering --- Engineering --- Industrial design --- Strains and stresses --- Complexity, Computational --- Design --- Computer science—Mathematics. --- Control engineering. --- Calculus of variations. --- Probabilities. --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Isoperimetrical problems --- Variations, Calculus of --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Automation --- Programmable controllers