Markov Chains

Gibbs Fields, Monte Carlo Simulation, and Queues

Pierre Bremaud - Collection Texts in Applied Mathematics

444 pages, parution le 01/01/2001

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Résumé

This book discusses both the theory and applications of Markov chains. The author studies both discrete-time and continuous-time chains and connected topics such as finite Gibbs fields, non-homogeneous Markov chains, discrete time regenerative processes, Monte Carlo simulation, simulated annealing, and queueing networks are also developed in this accessible and self-contained text. The text is firstly an introduction to the theory of stochastic processes at the undergraduate or beginning graduate level. Its primary objective is to initiate the student to the art of stochastic modelling. The treatment is mathematical, with definitions, theorems, proofs and a number of classroom examples which help the student to fully grasp the content of the main results. Problems of varying difficulty are proposed at the close of each chapter. The text is motivated by significant applications and progressively brings the student to the borders of contemporary research. Students and researchers in operations research and electrical engineering as well as in physics, biology and the social sciences will find this book of interest.

Written for:

Senior undergraduates, graduates

Sommaire

Preface
Probability Review
Discrete Time Markov Models
Recurrence and Ergodicity
Long Run Behavior
Lyapunov Functions and Martingales
Eigenvalues and Nonhomogeneous Markov Chains
Gibbs Fields and Monte Carlo Simulation
Continuous-Time Markov Models
Poisson Calculus and Queues
Appendix
Bibliography
Author Index
Subject Index

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Pierre Bremaud
Collection	Texts in Applied Mathematics
Parution	01/01/2001
Nb. de pages	444
Couverture	Relié
Intérieur	Noir et Blanc
EAN13	9780387985091
ISBN13	978-0-387-98509-1