Probability And Stochastic Processes: A Friendly Introduction For Electrical And Computer Engineers, 3rd Edition

The present reprint contains all of the articles accepted and published in the Special Issue "Probability and Stochastic Processes with Applications to Communications, Systems and Networks" from the MDPI Mathematics journal. This Special Issue is devoted to probability, statistics, stochastic processes and their different applications in system and network analysis. The Special Issue includes works related to the analysis and applications of different queuing models, which begin with general approaches in modeling queuing systems and networks; an analysis of probabilistic and statistical methods in telecommunication; an asymptotic analysis of queuing networks in the condition of a large load; general complex networks and their structures, e.g., topology and graph theory; mathematical methods and models in smart cities; exclusive statistical methods, such as statistical estimates in bio/ecology, medicine and neural networks; and studies that estimate parameters in complex technical systems, etc. We hope that the scientific results collected in this reprint will help foster future research related to probability, stochastic processes and their applications.

This book provides the elements of probability and stochastic processes of direct interest to the applied sciences where probabilistic models play an important role, most notably in the information and communications sciences, computer sciences, operations research, and electrical engineering, but also in fields like epidemiology, biology, ecology, physics, and the earth sciences.

This book presents applied probability and stochastic processes in an elementary but mathematically precise manner, with numerous examples and exercises to illustrate the range of engineering and science applications of the concepts. The book is designed to give the reader an intuitive understanding of probabilistic reasoning, in addition to an understanding of mathematical concepts and principles. The initial chapters present a summary of probability and statistics and then Poisson processes, Markov chains, Markov processes and queuing processes are introduced. Advanced topics include simulation, inventory theory, replacement theory, Markov decision theory, and the use of matrix geometric procedures in the analysis of queues.

These notes were written as a result of my having taught a "nonmeasure theoretic" course in probability and stochastic processes a few times at the Weizmann Institute in Israel. I have tried to follow two principles. The first is to prove things "probabilistically" whenever possible without recourse to other branches of mathematics and in a notation that is as "probabilistic" as possible. Thus, for example, the asymptotics of pn for large n, where P is a stochastic matrix, is developed in Section V by using passage probabilities and hitting times rather than, say, pulling in Perron­ Frobenius theory or spectral analysis. Similarly in Section II the joint normal distribution is studied through conditional expectation rather than quadratic forms.