Modeling and Analysis of Stochastic Systems
Modeling and Analysis of Stochastic Systems, Second Edition
Texts in Statistical Science Series
Chapman & Hall
V.G. Kulkarni, Professor of Statistics and Operations Research USAHardback 2009
- Presents a systematic and logical treatment of each class of stochastic process, including discrete- and continuous-time Markov chains and Poisson, renewal, regenerative, semi-Markov, Markov regenerative, and diffusion processes
- Provides a detailed account of Markov models
- Explains how first passage times play an important role in the applications and theory of limiting behavior
- Covers the important topic of phase-type distributions
- Demonstrates the use of queuing models in several applications
- Contains modeling, computational, and conceptual exercises
- Offers a collection of MATLAB®-based programs that can be downloaded from the author’s website
Solutions manual available for qualifying instructors
Based on the author’s more than 25 years of teaching experience, Modeling and Analysis of Stochastic Systems, Second Edition covers the most important classes of stochastic processes used in the modeling of diverse systems, from supply chains and inventory systems to genetics and biological 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. Along with reorganizing the material, this edition revises and adds new exercises and examples.
New to the Second Edition
- A new chapter on diffusion processes that gives an accessible and non-measure-theoretic treatment with applications to finance
- A more streamlined, application-oriented approach to renewal, regenerative, and Markov regenerative processes
- Two appendices that collect relevant results from analysis and differential and difference equations
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, students will be well-equipped to build and analyze useful stochastic models for various situations.
A collection of MATLAB®-based programs can be downloaded from the author’s website and a solutions manual is available for qualifying instructors.