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Markov Processes and Applications

include: * The Monte Carlo method, discrete time Markov chains, the Poisson process and continuous time jump Markov processes.* An introduction to;

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An Introduction to Markov Processes

Provides a more accessible introduction than other books on Markov processes by emphasizing the structure of the subject and avoiding;

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Semi-Dirichlet Forms and Markov Processes

setting, the stochastic calculus is not well established yet. In this monograph, we intend to give an introduction to such calculus. Furthermore;

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Markov Chains and Decision Processes for Engineers and Managers

chains with rewards so as to maximize expected rewards. An introduction to state reduction and hidden Markov chains rounds out the coverage;

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Markov Chains and Decision Processes for Engineers and Managers

chains with rewards so as to maximize expected rewards. An introduction to state reduction and hidden Markov chains rounds out the coverage;

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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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Controlled Markov Processes and Viscosity Solutions

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

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Markov Processes

Clear, rigorous, and intuitive, Markov Processes provides a bridge from an undergraduate probability course to a course in stochastic;

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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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Finite Markov Processes and Their Applications

introduction to the study of continuous parameter stochastic processes.;

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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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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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Understanding Markov Chains

This book provides an undergraduate-level introduction to discrete and continuous-time Markov chains and their applications, with a;

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Introduction to Stochastic Models

anyone looking for an introduction to stochastic models, will find this title of invaluable use.;

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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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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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Orthogonal Polynomials in the Spectral Analysis of Markov Processes

In pioneering work in the 1950s, S. Karlin and J. McGregor showed that probabilistic aspects of certain Markov processes can be studied by;

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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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An Introduction to Stochastic Processes with Applications to Biology

An Introduction to Stochastic Processes with Applications to Biology, Second Edition presents the basic theory of stochastic processes;

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First Look At Stochastic Processes, A

martingales. The final chapter provides a brief introduction to Brownian motion, Markov processes in continuous time and space, Poisson processes, and;

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First Look At Stochastic Processes, A

martingales. The final chapter provides a brief introduction to Brownian motion, Markov processes in continuous time and space, Poisson processes, and;

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Markov Processes from K. Ito's Perspective (AM-155)

Kolmogorov-Feller theory of Markov processes. Starting with the geometric ideas that guided him, this book gives an account of Ito's program. The;

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Statistical Inference for Piecewise-deterministic Markov Processes

processes are characterized by an embedded Markov chain corresponding to the position of the process right after the jumps.;

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Markov Renewal and Piecewise Deterministic Processes

deterministic functions of CSMPs (Completed Semi-Markov Processes), i.e. semi-Markov processes completed to become Markov processes. This remark leads;

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An Introduction to Stochastic Processes

A vigorous response to the challenges of incorporating computer use into the teaching and learning of stochastic processes, this book takes;

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