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Probability and Stochastic Processes : A Friendly Introduction for Electrical and Computer Engineers by Roy D. Yates and David J. Goodman (1998, Hardcover)
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About this product
Product Identifiers
PublisherWiley & Sons, Incorporated, John
ISBN-100471178373
ISBN-139780471178378
eBay Product ID (ePID)330897
Product Key Features
Number of Pages480 Pages
Publication NameProbability and Stochastic Processes : a Friendly Introduction for Electrical and Computer Engineers
LanguageEnglish
Publication Year1998
SubjectProbability & Statistics / Stochastic Processes, Probability & Statistics / General, Telecommunications
TypeTextbook
AuthorRoy D. Yates, David J.GOODMAN
Subject AreaMathematics, Technology & Engineering
FormatHardcover
Dimensions
Item Height0.9 in
Item Weight30.5 Oz
Item Length9.5 in
Item Width7.7 in
Additional Product Features
Intended AudienceCollege Audience
LCCN98-024820
Dewey Edition21
IllustratedYes
Dewey Decimal519.2
Table Of ContentExperiments, Models, and Probabilities. Discrete Random Variables. Multiple Discrete Random Variables. Continuous Random Variables. Multiple Continuous Random Variables. Stochastic Processes. Sums of Random Variables. The Sample Mean. Statistical Inference. Random Signal Processing. Renewal Processes and Markov Chains. Appendices. References. Index.
SynopsisApplications of probability theory appear throughout modern society, such as state lotteries, weather forecasts, and insurance prices. Professionals use probability theory as an astute tool for decision making. Electrical and computer engineers use probability to design computer networks, test integrated circuits, and evaluate communications systems., 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 either 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., 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 either 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.
LC Classification NumberQA273.Y384 1999
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