Probability, Statistics, and Random Processes For Electrical Engineering (3rd Edition) + Solutions

Book Preface

This book provides a carefully motivated, accessible, and interesting introduction to probability, statistics, and random processes for electrical and computer engineers.The complexity of the systems encountered in engineering practice calls for an understanding of probability concepts and a facility in the use of probability tools.The goal of the introductory course should therefore be to teach both the basic theoretical concepts and techniques for solving problems that arise in practice. The third edition of this book achieves this goal by retaining the proven features of previous editions:

• Relevance to engineering practice
• Clear and accessible introduction to probability
• Computer exercises to develop intuition for randomness
• Large number and variety of problems
• Curriculum flexibility through rich choice of topics
• Careful development of random process concepts.
This edition also introduces two major new features:
• Introduction to statistics
• Extensive use of MATLAB©/Octave.

RELEVANCE TO ENGINEERING PRACTICE

Motivating students is a major challenge in introductory probability courses. Instructors need to respond by showing students the relevance of probability theory to engineering practice. Chapter 1 addresses this challenge by discussing the role of probability models in engineering design. Practical current applications from various areas of electrical and computer engineering are used to show how averages and relative frequencies provide the proper tools for handling the design of systems that involve randomness.These application areas include wireless and digital communications, digital media and signal processing, system reliability, computer networks, and Web systems. These areas are used in examples and problems throughout the text.

ACCESSIBLE INTRODUCTION TO PROBABILITY THEORY

Probability theory is an inherently mathematical subject so concepts must be presented carefully, simply, and gradually.The axioms of probability and their corollaries are developed in a clear and deliberate manner.The model-building aspect is introduced through the assignment of probability laws to discrete and continuous sample spaces.The notion of a single discrete random variable is developed in its entirety, allowing the student to focus on the basic probability concepts without analytical complications. Similarly, pairs of random variables and vector random variables are discussed in separate chapters.

The most important random variables and random processes are developed in systematic fashion using model-building arguments. For example, a systematic development of concepts can be traced across every chapter from the initial discussions on coin tossing and Bernoulli trials, through the Gaussian random variable, central limit theorem, and confidence intervals in the middle chapters, and on to the Wiener process and the analysis of simulation data at the end of the book.The goal is to teach the student not only the fundamental concepts and methods of probability, but to also develop an awareness of the key models and their interrelationships.