Markov chains : Gibbs fields, Monte Carlo simulation and queues
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Bibliographic Information
Markov chains : Gibbs fields, Monte Carlo simulation and queues
（Texts in applied mathematics, 31）
Springer, c2020
2nd ed
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Note
Bibliography: p. 545-552
Includes index
Description and Table of Contents
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.
Table of Contents
Preface.- 1 Probability Review.- 2 Discrete-Time Markov Chains.- 3 Recurrence and Ergodicity.- 4 Long-Run Behavior.- 5 Discrete-Time Renewal Theory.- 6 Absorption and Passage Times.- 7 Lyapunov Functions and Martingales.- 8 Random Walks on Graphs.- 9 Convergence Rates.- 10 Markov Fields on Graphs.- 11 Monte Carlo Markov Chains.- 12 Non-homogeneous Markov Chains.- 13 Continuous-Time Markov Chains.- 14 Markovian Queueing Theory.- Appendices.- Bibliography.- Index.
by "Nielsen BookData"