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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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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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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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Ergodic Behavior of Markov Processes

The general topic of this book is the ergodic behavior of Markov processes. A detailed introduction to methods for proving ergodicity and;

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Introduction to Hidden Semi-Markov Models

to the subject by first developing the theory of Markov processes in an elementary discrete time, finite state framework suitable for senior;

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Finite Markov Processes and Their Applications

A self-contained treatment of finite Markov chains and processes, this text covers both theory and applications. Author Marius Iosifescu;

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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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A First Course in Probability and Markov Chains

with an introduction to Poisson processes and Continuous Time Discrete Markov Chains. This book also looks at making use of measure theory;

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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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General Irreducible Markov Chains and Non-Negative Operators

The purpose of this book is to present the theory of general irreducible Markov chains and to point out the connection between this and the;

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Stochastic Processes

process, and the Markov renewal process. Many examples are cited from reliability models to show the reader how to apply stochastic processes;

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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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Markov Chains

Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random;

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Asymptotic Properties of Permanental Sequences

in stochastic processes, infinitely divisible processes and Markov chains.;

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Stochastic Modeling and Analysis of Telecom Networks

as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability;

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Automated Technology for Verification and Analysis

were organized in topical sections named: keynote; Markov models, chains, and decision processes; counter systems, automata; parallelism;

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Markov Processes

processes and also as a reference for those that want to see detailed proofs of the theorems of Markov processes. It contains copious computational;

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Statistical Inference for Piecewise-deterministic Markov Processes

-deterministic Markov processes offers a detailed and comprehensive survey of state-of-the-art results. It covers a wide range of general processes as;

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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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Applications of Markov Chains in Chemical Engineering

, despite the widespread use of Markov chains in many areas of science and technology, their applications in chemical engineering have been;

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Markov Processes for Stochastic Modeling

This book presents an algebraic development of the theory of countable state space Markov chains with discrete- and continuous-time;

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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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Martingales and Markov Chains

notions of conditional expectations and stochastic processes, then set the stage for each set of exercises by recalling the relevant elements;

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Martingales and Markov Chains

notions of conditional expectations and stochastic processes, then set the stage for each set of exercises by recalling the relevant elements;

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Structured Stochastic Matrices of M/G/1 Type and Their Applications

This book deals with Markov chains and Markov renewal processes (M/G/1 type). It discusses numerical difficulties which are apparently;

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