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Wiley india Pvt. Ltd Probability and Stochastic Processes 2011 Edition by Roy D. Yates
This user-friendly resource helps readers grasp the concepts of probability and stochastic processes, so they can apply them in professional engineering practice. The book presents concepts clearly as a sequence of building blocks that are identified either as an axiom, definition, or theorem. This approach provides a better understanding of the material, which can be used to solve practical problems.What Does Winning the Lottery Have To do with Engineering? Whether you're trying to win millions in the lottery or designing a complex computer network, you're applying probability theory. Although you encounter probability applications everywhere, the theory can be deceptively difficult to learn and apply correctly. This text will help you grasp the concepts of probability and stochastic processes and apply them throughout your careers. These concepts are clearly presented throughout the book as a sequence of building blocks that are clearly identified as an axiom, definition, or theorem. This approach provides you with a better understanding of the material, which you'll be able to use to solve practical problems.Key Features:The text follows a single model that begins with an experiment consisting of a procedure and observations.The mathematics of discrete random variables appears separately from the mathematics of continuous random variables.Stochastic processes are introduced in Chapter 6, immediately after the presentation of discrete and continuous random variables. Subsequent material, including central limit theorem approximations, laws of large numbers, and statistical inference, then use examples that reinforce stochastic process concepts.An abundance of exercises are provided that help students learn how to put the theory to use.About The AuthorDr. Roy Yates received the B.S.E. degree in 1983 from Princeton University, and the S.M. and Ph.D. degrees in 1986 and 1990 from M.I.T., all in Electrical Engineering. Since 1990, he has been with the Wireless Information Networks Laboratory (WINLAB) and the ECE department at Rutgers, University. He is currently an associate professor.David J. Goodman is Director of WINLAB and a Professor of Electrical and Computer Engineering at Rutgers University. Before coming to Rutgers, he enjoyed a twenty year research career at Bell Labs where he was a Department Head in Communications Systems Research. He has made fundamental contributions to digital signal processing, speech coding, and wireless information networks.Table Of ContentsExperiments, Models, and Probabilities.Discrete Random Variables.Continuous Random Variables.Pairs of Random Variables.Random Vectors.Sums of Random Variables.Parameter Estimation Using the Sample Mean.Hypothesis Testing.Estimation of a Random Variable.Stochastic Processes.Random Signal Processing.Markov Chains.Appendix A: Families of Random Variables.Appendix B: A Few Math Facts.References.Index.