9780471272144-0471272140-Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers

Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers

ISBN-13: 9780471272144
ISBN-10: 0471272140
Edition: 2
Author: Roy D. Yates, David J. Goodman
Publication date: 2004
Publisher: Wiley
Format: Hardcover 544 pages
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Book details

ISBN-13: 9780471272144
ISBN-10: 0471272140
Edition: 2
Author: Roy D. Yates, David J. Goodman
Publication date: 2004
Publisher: Wiley
Format: Hardcover 544 pages

Summary

Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers (ISBN-13: 9780471272144 and ISBN-10: 0471272140), written by authors Roy D. Yates, David J. Goodman, was published by Wiley in 2004. With an overall rating of 4.5 stars, it's a notable title among other Applied (Mathematics) books. You can easily purchase or rent Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers (Hardcover, Used) from BooksRun, along with many other new and used Applied books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.62.

Description

This user-friendly resource will help you grasp the concepts of probability and stochastic processes, so you 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.

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.
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