This is the standard textbook for courses on probability and statistics, not substantially updated. While helping students to develop their problem-solving skills, the author motivates students with practical applications from various areas of ECE that demonstrate the relevance of probability theory to engineering practice. Included are chapter overviews, summaries, checklists of important terms, annotated references, and a wide selection of fully worked-out real-world examples. In this edition, the Computer Methods sections have been updated and substantially enhanced and new problems have been added.
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This book offers an interesting, straightforward introduction to probability and random processes. While helping readers to develop their problem-solving skills, the book enables them to understand how to make the transition from real problems to probability models for those problems. To keep users motivated, the author uses a number of practical applications from various areas of electrical and computer engineering that demonstrate the relevance of probability theory to engineering practice. Discrete-time random processes are used to bridge the transition between random variables and continuous-time random processes. Additional material has been added to the second edition to provide a more substantial introduction to random processes.
The book's first five chapters form the basis of a traditional, introduction to probability and random variables. In addition to the standard topics, it offers optional sections on modeling, computer methods, combinatories, reliability, and entropy. Chapters 4 through 9 can accommodate a one-semester senior/first-year graduate course on random processes and linear systems, as well as Markov chains and queuing theory. Additional coverage includes cyclostationary random processes, Fourier series and Karhunen-Loeve expansion, continuity, derivatives and integrals, amplitude modulation. Wiener and Kalman filters, and time reversed Markov chains.
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Book Description Prentice Hall, 2008. Book Condition: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: 1. Probability Models in Electrical and Computer Engineering. Mathematical models as tools in analysis and design. Deterministic models. Probability models. Statistical regularity. Properties of relative frequency. The axiomatic approach to a theory of probability. Building a probability model. A detailed example: a packet voice transmission system. Other examples. Communication over unreliable channels. Processing of random signals. Resource sharing systems. Reliability of systems. Overview of book. Summary. Problems. 2. Basic Concepts of Probability Theory. Specifying random experiments. The sample space. Events. Set operations. The axioms of probability. Discrete sample spaces. Continuous sample spaces. Computing probabilities using counting methods. Sampling with replacement and with ordering. Sampling without replacement and with ordering. Permutations of n distinct objects. Sampling without replacement and without ordering. Sampling with replacement and without ordering. Conditional probability. Bayes'Rule. Independence of events. Sequential experiments. Sequences of independent experiments. The binomial probability law. The multinomial probability law. The geometric probability law. Sequences of dependent experiments. A computer method for synthesizing randomness: random number generators. Summary. Problems. 3. Random Variables. The notion of a random variable. The cumulative distribution function. The three types of random variables. The probability density function. Conditional cdf's and pdf's. Some important random variables. Discrete random variables. Continuous random variables. Functions of a random variable. The expected value of random variables. The expected value of X. The expected value of Y = g(X). Variance of X. The Markov and Chebyshev. Bookseller Inventory # ABE_book_new_0131471228
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