Probability, random variables, and stochastic processes (McGraw-Hill series in electrical engineering)

Athanasios Papoulis

ISBN 10: 0070484686 / ISBN 13: 9780070484689

Published by McGraw-Hill Inc.,US, 1984

About this Item

Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.

Seller Inventory # Scanned0070484686

Bibliographic Details

• Title: Probability, random variables, and stochastic processes (McGraw-Hill series in electrical engineering)
• Publisher: McGraw-Hill Inc.,US
• Publication Date: 1984
• Language: English
• ISBN 10: 0070484686
• ISBN 13: 9780070484689
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: new

About this title

Synopsis

Designed for graduate-level courses, this text has defined the course of study in probability theory, highly regarded for its strong mathematical orientation and comprehensive coverage. The book classifies topics in probability, random variables, and stochastic processes very logically, carefully incorporating a wide range of illustrations and applications. This edition contains a substantial revision of Parts II & III with greater emphasis on realistic methods of spectral estimation and analysis, and many new problems, examples and applications.

Review

This text is a classic in probability, statistics, and estimation and in the application of these fields to modern engineering problems. Probability, Random Variables, and Stochastic Processes assumes a strong college mathematics background. The first half of the text develops the basic machinery of probability and statistics from first principles while the second half develops applications of the basic theory. Topics in the first section include probability distributions and densities, random variables and vectors, expectations, covariance, correlations, functions of random variables and vectors, and conditional distributions and densities. In this third edition of the text, the second half of the book has been substantially updated and expanded to include new or revised discussions of the following topics: mean square estimation, likelihood tests, maximum entropy methods, Monte Carlo techniques, spectral representations and estimation, sampling theory, bispectra and system identification, cyclostationary processes, deterministic signals in noise, and the Wiener and Kalman filters. Probability, Random Variables, and Stochastic Processes covers a remarkable density of material and the clarity of both presentation and notation make this book invaluable as a text and a reference.