Schaum's Outline of Probability, Random Variables, and Random Processes, 3/E (Enhanced Ebook)

Nonfiction, Science & Nature, Mathematics, Mathematical Analysis, Reference & Language, Study Aids, Tests
Cover of the book Schaum's Outline of Probability, Random Variables, and Random Processes, 3/E (Enhanced Ebook) by Hwei P Hsu, McGraw-Hill Education
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Author: Hwei P Hsu ISBN: 9780071824842
Publisher: McGraw-Hill Education Publication: March 14, 2014
Imprint: McGraw-Hill Education Language: English
Author: Hwei P Hsu
ISBN: 9780071824842
Publisher: McGraw-Hill Education
Publication: March 14, 2014
Imprint: McGraw-Hill Education
Language: English

The ideal review for the thousands of electrical engineering college students who enroll in probability, variables, and processes courses

Schaum's Outline of Probability, Random Variables, and Random Processes mirrors the courses in scope and sequence to help enrolled students understand basic concepts and offer extra practice on topics such as bivariate random variables, joint distribution functions, moment generating functions, Poisson processes, Wiener processes, power spectral densities, and white noise. Coverage will also include response of linear systems to random outputs, Fourier series and Karhunen-Loeve expansions, Fourier transform of random processes, parameter estimation, Bayes' estimation, and mean square estimation.

Features

  • Outline format supplies a concise guide to the standard college courses in probability, variables, and processes
  • 405 solved problems
  • New chapter on statistical communication theory
  • Additional material on distributions, the Markov Process, and martingales
  • Supports all the major textbooks for probability, variables, and processes courses
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

The ideal review for the thousands of electrical engineering college students who enroll in probability, variables, and processes courses

Schaum's Outline of Probability, Random Variables, and Random Processes mirrors the courses in scope and sequence to help enrolled students understand basic concepts and offer extra practice on topics such as bivariate random variables, joint distribution functions, moment generating functions, Poisson processes, Wiener processes, power spectral densities, and white noise. Coverage will also include response of linear systems to random outputs, Fourier series and Karhunen-Loeve expansions, Fourier transform of random processes, parameter estimation, Bayes' estimation, and mean square estimation.

Features

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