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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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Markov Chains and Decision Processes for Engineers and Managers

service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly;

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Markov Chains and Decision Processes for Engineers and Managers

service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly;

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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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Dynamic and Stochastic Multi-Project Planning

This book deals with dynamic and stochastic methods for multi-project planning. Based on the idea of using queueing networks for the;

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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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Planning with markov Decision Processes

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with;

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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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Continuous-Time Markov Decision Processes

This book offers a systematic and rigorous treatment of continuous-time Markov decision processes, covering both theory and possible;

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Continuous-Time Markov Decision Processes

This book offers a systematic and rigorous treatment of continuous-time Markov decision processes, covering both theory and possible;

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Elements Of Stochastic Modelling

of probability theory and then covers the following topics: Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory;

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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 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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Planning Algorithms

theory, Markov decision processes, and information spaces, which are the 'configuration spaces' of all sensor-based planning problems. The last;

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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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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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Markov Processes

of finite state, discrete time Markov processes. Building on this, the text deals with the discrete time, infinite state case and provides;

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Markov Decision Processes

field to have now a complete volume (with more than 600 pages) devoted to this topic...Markov Decision Processes: Discrete Stochastic Dynamic;

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Ergodic Behavior of Markov Processes

and Processes Markov Chains with Discrete State Spaces General Markov Chains: Ergodicity in Total Variation;

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Markov Processes and Applications

This well-written book provides a clear and accessible treatment of the theory of discrete and continuous-time Markov chains, with an;

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Markov-modulated Processes And Semiregenerative Phenomena

The book presents a coherent treatment of Markov random walks and Markov additive processes together with their applications. Part I;

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Markov Processes for Stochastic Modeling

Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state;

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Semi-Dirichlet Forms and Markov Processes

This book deals with analytic treatments of Markov processes. Symmetric Dirichlet forms and their associated Markov processes are important;

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Statistical Topics and Stochastic Models for Dependent Data with Applications

corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov;

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