This book develops systematically and rigorously, yet in an expository and lively manner, the evolution of general random processes and their large time properties such as transience, recurrence, and convergence to steady states. The emphasis is on the most important classes of these processes from the viewpoint of theory as well as applications, namely, Markov processes. The book features very broad coverage of the most applicable aspects of stochastic processes, including sufficient material for self-contained courses on random walks in one and multiple dimensions; Markov chains in discrete and continuous times, including birth-death processes; Brownian motion and diffusions; stochastic optimization; and stochastic differential equations. This book is for graduate students in mathematics, statistics, science and engineering, and it may also be used as a reference by professionals in diverse fields whose work involves the application of probability.
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A very broad coverage of the most applicable aspects of stochastic processes. The emphasis is on the most important classes of these processes from the viewpoint of theory as well as applications, namely, Markov processes. It is for graduate students, but will also be useful to professionals as a reference.
Provides a clear and concise explanation of stochastic theory with an emphasis on computation and examples. This practical graduate text develops theory at an understandable technical level and offers challenging examples to underscore theoretical principles. A special section at the end of each chapter offers additional examples and applications to reinforce the theory in the chapter exercises, and the self-contained chapter on measure theory can be used as a short course or integrated into a general stochastic processes class. The sample course outlines that illustrate the different types of courses that could be based on the text will be of particular interest to professors/instructors.
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