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Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues

Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues

Authors
Publisher Springer, Berlin
Year
Pages 444
Version paperback
Language English
ISBN 9781441931313
Categories Probability & statistics
Delivery to United States

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Book description

Primarily an introduction to the theory of stochastic processes at the undergraduate or beginning graduate level, the primary objective of this book is to initiate students in the art of stochastic modelling. However it is motivated by significant applications and progressively brings the student to the borders of contemporary research. Examples are from a wide range of domains, including operations research and electrical engineering. Researchers and students in these areas as well as in physics, biology and the social sciences will find this book of interest. 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.

Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues

Table of contents

Preface 1 Probability Review 2 Discrete Time Markov Models 3 Recurrence and Ergodicity 4 Long Run Behavior 5 Lyapunov Functions and Martingales 6 Eigenvalues and Nonhomogeneous Markov Chains 7 Gibbs Fields and Monte Carlo Simulation 8 Continuous-Time Markov Models 9 Poisson Calculus and Queues Appendix Bibliography Author Index Subject Index

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