Description
Modeling and Analysis of Stochastic Systems (3rd Ed.)
Chapman & Hall/CRC Texts in Statistical Science Series
Language: EnglishSubjects for Modeling and Analysis of Stochastic Systems:
Keywords
Transition Probability Matrix; Iid Exp; Positive Recurrent; Iid Random Variables; Retrial Queue; Computational Exercise; Single Server Queue; Inter-event Times; Typical Sample Path; Sample Paths; Simple Random Walk; Renewal Type Equation; Countable State Space; Renewal Reward Process; Continuous Time Stochastic Process; Inter-renewal Time; Irreducible CTMC; Jackson Network; Queueing Network; Queueing System; Ito Integral; Buy Order; IBM Stock; Phase Type Random Variables; Renewal Function
Publication date: 12-2020
· 15.6x23.4 cm · Paperback
Publication date: 10-2016
· 15.6x23.4 cm · Hardback
Description
/li>Contents
/li>Biography
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Building on the author?s more than 35 years of teaching experience, Modeling and Analysis of Stochastic Systems, Third Edition, covers the most important classes of stochastic processes used in the modeling of diverse systems. For each class of stochastic process, the text includes its definition, characterization, applications, transient and limiting behavior, first passage times, and cost/reward models.
The third edition has been updated with several new applications, including the Google search algorithm in discrete time Markov chains, several examples from health care and finance in continuous time Markov chains, and square root staffing rule in Queuing models. More than 50 new exercises have been added to enhance its use as a course text or for self-study. The sequence of chapters and exercises has been maintained between editions, to enable those now teaching from the second edition to use the third edition.
Rather than offer special tricks that work in specific problems, this book provides thorough coverage of general tools that enable the solution and analysis of stochastic models. After mastering the material in the text, readers will be well-equipped to build and analyze useful stochastic models for real-life situations.
Vidyadhar G. Kulkarni
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