Probability and Random Processes for Electrical Engineering (Addison-Wesley Series in Electrical & Computer Engineering)
This textbook offers an interesting, straightforward introduction to probability and random processes. While helping students to develop their problem-solving skills, the book enables them to understand how to make the transition from real problems to probability models for those problems. To keep students motivated, the author uses a number of practical applications from various areas of electrical and computer engineering that demonstrate the relevance of probability theory to engineering practice. Discrete-time random processes are used to bridge the transition between random variables and continuous-time random processes. Additional material has been added to the second edition to provide a more substantial introduction to random processes.
Why Read This Book
You will learn the probabilistic and random-process foundations that underlie modern DSP, communications, radar, and audio/speech systems, presented with an engineer's eye toward practical modeling and problem solving. Leon-Garcia emphasizes building intuition from examples and discrete-time processes so you can move confidently from real signals to tractable probability models and spectral analyses.
Who Will Benefit
Upper-level undergraduate or graduate electrical/computer engineers and practicing signal-processing or communications engineers who need a solid, application-focused grounding in probability and random processes.
Level: Intermediate — Prerequisites: Calculus (including multivariable), linear algebra, and basic signals & systems; prior exposure to elementary probability (random variables and distributions) is helpful but not strictly required.
Key Takeaways
- Model real-world signals as random variables and random processes suitable for DSP and communications analysis.
- Compute and interpret autocorrelation, cross-correlation, and power spectral density for discrete- and continuous-time processes.
- Analyze how linear systems affect stochastic inputs and predict output statistics for filtering and communications links.
- Apply limit theorems and transform methods to derive approximations used in spectral estimation and detection.
- Formulate and solve basic linear estimation/Wiener-filter problems and understand the statistical foundations for adaptive filtering.
- Translate discrete-time random process results into continuous-time spectral analysis used in radar, audio, and speech applications.
Topics Covered
- Basic Probability Concepts and Random Variables
- Joint Distributions, Conditional Probability and Expectation
- Transform Methods: Characteristic and Moment-Generating Functions
- Limit Theorems and Convergence of Random Variables
- Discrete-Time Random Processes: Definitions and Examples
- Stationarity, Ergodicity, and Correlation Functions
- Power Spectral Density and Spectral Representations
- Linear Systems Driven by Random Inputs
- Gaussian Processes and Simplifications for Engineering
- Point Processes and Renewal Models (including Poisson processes)
- Markov Chains and Continuous-Time Markov Processes
- Estimation Basics: Linear Estimation and Wiener Filtering
- Spectral Estimation and Introductory Detection Concepts
- Bridging Discrete- and Continuous-Time Random Processes
Languages, Platforms & Tools
How It Compares
More application-oriented and accessible than Papoulis' classic treatment, Leon-Garcia emphasizes discrete-time bridges to practice; compared with Van Trees' detection/estimation texts, it focuses more on probabilistic foundations than advanced detection theory.
