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Learning Representation and Control in Markov Decision Processes

Learning Representation and Control in Markov Decision Processes describes methods for automatically compressing Markov decision processes;

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Modern Trends in Controlled Stochastic Processes:

methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information;

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Markov Decision Processes in Artificial Intelligence

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as;

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Selected Topics on Continuous-Time Controlled Markov Chains and Markov Games

This book concerns continuous-time controlled Markov chains, also known as continuous-time Markov decision processes. They form a class;

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Probabilistic Graphical Models

fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the;

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Probabilistic Graphical Models

fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the;

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Markov Chains and Decision Processes for Engineers and Managers

Markov decision processes in a single volume, Markov Chains and Decision Processes for Engineers and Managers supplies a highly detailed;

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Markov Chains and Decision Processes for Engineers and Managers

Markov decision processes in a single volume, Markov Chains and Decision Processes for Engineers and Managers supplies a highly detailed;

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Handbook of Markov Decision Processes

Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter;

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Handbook of Markov Decision Processes

Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter;

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Markov Decision Processes

Examines several fundamentals concerning the manner in which Markov decision problems may be properly formulated and the determination;

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Approximate Iterative Algorithms

in functional analysis and probability theory. Extensive applications to Markov decision processes are presented. This volume is intended for;

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Approximate Iterative Algorithms

in functional analysis and probability theory. Extensive applications to Markov decision processes are presented. This volume is intended for;

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Competitive Markov Decision Processes

This book is intended as a text covering the central concepts and techniques of Competitive Markov Decision Processes. It is an attempt to;

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Competitive Markov Decision Processes

This book is intended as a text covering the central concepts and techniques of Competitive Markov Decision Processes. It is an attempt to;

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Probabilistic Graphical Models

, and Markov decision processes. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical;

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Markov Chains

. The chapter then covers the basic theories and algorithms for hidden Markov models (HMMs) and Markov decision processes (MDPs). Chapter 2;

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Partially Observed Markov Decision Proce

social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision;

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Self-Learning Control of Finite Markov Chains

Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results;

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Self-Learning Control of Finite Markov Chains

Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results;

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Controlled Markov Processes and Viscosity Solutions

This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It;

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Controlled Markov Processes and Viscosity Solutions

This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It;

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Visual Analysis of Behaviour

, modelling rare behaviours, and man-in-the-loop active learning; examines multi-camera behaviour correlation, person re-identification, and;

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Invariant Markov Processes Under Lie Group Actions

ways to represent such processes in the spirit of the classical Levy-Khinchin representation. It interweaves probability theory, topology, and;

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Markov Processes and Applications

, Professor at Universite de Paris-Orsay, France. Markov processes is the class of stochastic processes whose past and future are conditionally;

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Semi-Markov Processes

Semi-Markov Processes: Applications in System Reliability and Maintenance is a modern view of discrete state space and continuous time;

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Quantitative Evaluation of Systems

submissions. They are organized in topical sections entitled: Markov processes; tools; sampling, inference, and optimization methods; Markov decision;

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