Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues (Texts in Applied Mathematics): 31 - Taschenbuch

EAN (ISBN-13): 9781441931313
ISBN (ISBN-10): 1441931317
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2010
Herausgeber: Springer
464 Seiten
Gewicht: 0,841 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2010-04-05T22:35:38+02:00 (Berlin)
Detailseite zuletzt geändert am 2023-07-26T08:15:27+02:00 (Berlin)
ISBN/EAN: 9781441931313

ISBN - alternative Schreibweisen:
1-4419-3131-7, 978-1-4419-3131-3
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: bremaud pierre
Titel des Buches: monte carlo simulation, markov chains gibbs fields

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Daten vom Verlag:

Autor/in: Pierre Bremaud
Titel: Texts in Applied Mathematics; Markov Chains - Gibbs Fields, Monte Carlo Simulation, and Queues
Verlag: Springer; Springer US
444 Seiten
Erscheinungsjahr: 2010-12-01
New York; NY; US
Gedruckt / Hergestellt in Vereinigte Staaten.
Gewicht: 0,863 kg
Sprache: Englisch
60,94 € (DE)
62,65 € (AT)
86,58 CHF (CH)
Not available, publisher indicates OP

BC; Previously published in hardcover; Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; Monte Carlo Simulation; Markov Chains; Markov model; Stochastic modelling; Martingale; electrical engineering; regenerative process; Stochastic model; modeling; Markov; simulation; operations research; ergodicity; linear optimization; stochastic process; B; Probability Theory and Stochastic Processes; Operations Research/Decision Theory; Electrical Engineering; Mathematics and Statistics; Stochastik; Unternehmensforschung; Management: Entscheidungstheorie; Elektrotechnik; BB; BB

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
This text is 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 modeling. Mathematical definitions, theorems, proofs, and a number of classroom examples help the student to fully grasp the content of the main results, and problems of varying difficulty are proposed at the end of each chapter. The material is accessible to students who know the basics of probability theory, but a review of probability is included to make the text largely self-contained. It brings students to the borders of current research covering more advanced topics such as Martingales, eigenvalue, Gibbs fields and Monte Carlo techniques.