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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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Applications of Discrete-time Markov Chains and Poisson Processes to Air Pollution Modeling and Studies

pollution. The impact of exposure to air pollutants on people's health is a very clear and well documented subject. Therefore, it is very important;

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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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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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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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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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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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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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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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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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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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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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Applied Discrete-Time Queues

in discrete-time can be set up as discrete-time Markov chains. Techniques such as matrix-analytic methods (MAM) that can used to analyze the resulting;

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Applied Discrete-Time Queues

in discrete-time can be set up as discrete-time Markov chains. Techniques such as matrix-analytic methods (MAM) that can used to analyze the resulting;

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Stochastic Modeling

Markov chains, Poisson processes, and continuous-time Markov chains. The theory developed is illustrated by a variety of examples surrounding;

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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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Probability An Introduction 2Nd

about branching processes, random walks, and continuous-time random processes such as the Poisson process. The final chapter is a fairly;

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Communication And Computer Networks

This book presents a unified approach to developing accurate discrete-time models of communication and computer networks, using discrete;

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Applied Stochastic Processes

Markovian processes, Markov Chains, Poisson processes, Brownian motion and queueing theory. The book also examines in detail special diffusion;

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Markov Chains and Decision Processes for Engineers and Managers

description of the construction and solution of Markov models that facilitates their application to diverse processes. Organized around Markov;

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Markov Chains and Decision Processes for Engineers and Managers

description of the construction and solution of Markov models that facilitates their application to diverse processes. Organized around Markov;

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Modeling and Analysis of Stochastic Systems

from health care and finance in continuous time Markov chains, and square root staffing rule in Queuing models. More than 50 new exercises have;

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Modeling and Analysis of Stochastic Systems

examples from health care and finance in continuous time Markov chains, and square root staffing rule in Queuing models. More than 50 new exercises;

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Performance Modeling of Communication Networks with Markov Chains

property, discrete time Markov chain (DTMC) and continuous time Markov chain (CTMC). We also discuss how to find the steady state distributions;

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Markov Chains

Markov Chains: Analytic and Monte Carlo Computations introduces the main notions related to Markov chains and provides explanations on how;

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Stochastic Modeling in Physical and Biological Sciences

Discusses basic definitions, important properties and results on Markov Chains giving examples to understand the intricacies of the 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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