Book description
This text introduces engineering students to probability theory and stochastic processes. Along with thorough mathematical development of the subject, the book presents intuitive explanations of key points in order to give students the insights they need to apply math to practical engineering problems. The first seven chapters contain the core material that is essential to any introductory course. In onesemester undergraduate courses, instructors can select material from the remaining chapters to meet their individual goals. Graduate courses can cover all chapters in one semester.
Table of contents
 Coverpage
 Features of this Text
 Titlepage
 Copyright
 Dedication
 Preface
 Contents
 1 Experiments, Models, and Probabilities
 2 Sequential Experiments
 3 Discrete Random Variables
 4 Continuous Random Variables

5 Multiple Random Variables
 5.1 Joint Cumulative Distribution Function
 5.2 Joint Probability Mass Function
 5.3 Marginal PMF
 5.4 Joint Probability Density Function
 5.5 Marginal PDF
 5.6 Independent Random Variables
 5.7 Expected Value of a Function of Two Random Variables
 5.8 Covariance, Correlation and Independence
 5.9 Bivariate Gaussian Random Variables
 5.10 Multivariate Probability Models
 5.11 MATLAB
 6 Probability Models of Derived Random Variables

7 Conditional Probability Models
 7.1 Conditioning a Random Variable by an Event
 7.2 Conditional Expected Value Given an Event
 7.3 Conditioning Two Random Variables by an Event
 7.4 Conditioning by a Random Variable
 7.5 Conditional Expected Value Given a Random Variable
 7.6 Bivariate Gaussian Random Variables: Conditional PDFs
 7.7 MATLAB
 8 Random Vectors
 9 Sums of Random Variables
 10 The Sample Mean
 11 Hypothesis Testing
 12 Estimation of a Random Variable

13 Stochastic Processes
 13.1 Definitions and Examples
 13.2 Random Variables from Random Processes
 13.3 Independent, Identically Distributed Random Sequences
 13.4 The Poisson Process
 13.5 Properties of the Poisson Process
 13.6 The Brownian Motion Process
 13.7 Expected Value and Correlation
 13.8 Stationary Processes
 13.9 Wide Sense Stationary Stochastic Processes
 13.10 CrossCorrelation
 13.11 Gaussian Processes
 13.12 MATLAB
 Appendix A Families of Random Variables
 Appendix B A Few Math Facts
 References
 Index
Product information
 Title: Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, 3rd Edition
 Author(s):
 Release date: January 2014
 Publisher(s): Wiley
 ISBN: 9781118324561
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