A First Course in Stochastic ModelsJohn Wiley and Sons, 22.07.2003 - 496 Seiten The field of applied probability has changed profoundly in the past twenty years. The development of computational methods has greatly contributed to a better understanding of the theory. A First Course in Stochastic Models provides a self-contained introduction to the theory and applications of stochastic models. Emphasis is placed on establishing the theoretical foundations of the subject, thereby providing a framework in which the applications can be understood. Without this solid basis in theory no applications can be solved.
A First Course in Stochastic Models is suitable for senior undergraduate and graduate students from computer science, engineering, statistics, operations resear ch, and any other discipline where stochastic modelling takes place. It stands out amongst other textbooks on the subject because of its integrated presentation of theory, algorithms and applications. |
Inhalt
| 1 | |
| 33 | |
| 81 | |
4 ContinuousTime Markov Chains | 141 |
5 Markov Chains and Queues | 187 |
6 DiscreteTime Markov Decision Processes | 233 |
7 SemiMarkov Decision Processes | 279 |
8 Advanced Renewal Theory | 307 |
9 Algorithmic Analysis of Queueing Models | 339 |
Appendices | 431 |
Index | 475 |
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applications approximation arrival process arrival rate arrive according assumed assumption asymptotic expansion average cost optimal batch buffer closed sets coefficient of variation compute constant continuous-time Markov chain customers arrive customers present cycle decision epochs define defined denote distribution with mean equals equilibrium distribution equilibrium equations Erlang Example exponentially distributed find finite first follows formula fraction of customers gamma distribution given independent random variables integer interarrival inventory iteration Laplace transform linear equations linear programming long-run average cost long-run average number long-run fraction M/G/1 queue Markov chain Xn matrix mean 1/µ messages number of customers one-step transition probabilities parameters Poisson process probability density probability distribution function problem process with rate proof queueing model queueing systems regenerative renewal process renewal theory renewal-reward repair result Section server solution station stationary policy stochastic process system of linear Theorem transient unit value-iteration algorithm values verify waiting-time probabilities
Verweise auf dieses Buch
Modellbildung und Simulation Hans-Joachim Bungartz,Stefan Zimmer,Martin Buchholz,Dirk Pflüger Keine Leseprobe verfügbar - 2009 |
