Features
Develops the ideas leading up to queueing theory Presents elements of parameter estimation and their application to communication theory Includes MATLAB functions Provides numerous examples and problems, including computer projects Offers an extensive set of lecture slides for download from www.crcpress.comA solutions manual is available with qualifying course adoptions.
Summary
Scientists and engineers must use methods of probability to predict the outcome of experiments, extrapolate results from a small case to a larger one, and design systems that will perform optimally when the exact characteristics of the inputs are unknown. While many engineering books dedicated to the advanced aspects of random processes and systems include background information on probability, an introductory text devoted specifically to probability and with engineering applications is long overdue.

Probability for Electrical and Computer Engineers provides an introduction to probability and random variables. Written in a clear and concise style that makes the topic interesting and relevant for electrical and computer engineering students, the text also features applications and examples useful to anyone involved in other branches of engineering or physical sciences. Chapters focus on the probability model, random variables and transformations, inequalities and limit theorems, random processes, and basic combinatorics. These topics are reinforced with computer projects available on the CRC Press Web site.

This unique book enhances the understanding of probability by introducing engineering applications and examples at the earliest opportunity, as well as throughout the text. Electrical and computer engineers seeking solutions to practical problems will find it a valuable resource in the design of communication systems, control systems, military or medical sensing or monitoring systems, and computer networks. INTRODUCTION
The Analysis of Random Experiments
Probability in Electrical and Computer Engineering
Outline of the Book.
THE PROBABILITY MODEL
The Algebra of Events
Probability of Events
Some Applications
Conditional Probability and Bayes' Rule
More Applications
Summary
RANDOM VARIABLES AND TRANSFORMATIONS
Discrete Random Variables
Common Discrete Probability Distributions
Continuous Random Variables
Common Continuous Probability Density Functions
CDF and PDF for Discrete and Mixed Random Variables
Transformation of Random Variables
Distributions Conditioned on an Event
Applications
Summary
EXPECTATION, MOMENTS, AND GENERATING FUNCTIONS
Expectation of a Random Variable
Moments of a Distribution
Generating Functions
Application: Entropy and Source Coding
Summary
TWO AND MORE RANDOM VARIABLES
Two Discrete Random Variables
Two Continuous Random Variables
Expectation and Correlation
Gaussian Random Variables
Multiple Random Variables
Sums of Some Common Random Variable
Random Vectors
An Application to Signal Detection
Summary
INEQUALITIES, LIMIT THEOREMS, AND PARAMETER ESTIMATION
Inequalities
Convergence and Limit Theorems
Estimation of Parameters
Maximum Likelihood Estimation
Application to Signal Estimation
Summary
RANDOM PROCESSES
Random Process
First and Second Moments of a Random Process
Properties: Independence, Stationarity, and Ergodicity
Power Spectral Density
Noise Sources
Response of Linear Systems
Summary
MARKOV AND POISSON RANDOM PROCESSES
The Poisson Model
Discrete-Time Markov Chains
Continuous-Time Markov Chains
Basic Queueing Theory
Summary
APPENDICES
Basic Combinatorics
The Unit Impulse
INDEX