Probability and Random Processes for Electrical Engineering (2nd Edition)
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 gain a clear, application-focused foundation in probability and random processes that directly maps to the problems you face in DSP, communications, radar, and speech processing. The book emphasizes building problem‑solving skills and shows how to translate real engineering problems into probability models, using discrete‑time processes to bridge to continuous‑time theory.
Who Will Benefit
Advanced undergraduates, graduate students, and practicing electrical engineers who need a practical, engineering‑oriented introduction to probability and random processes for DSP, communications, radar, or audio/speech work.
Level: Intermediate — Prerequisites: Single‑variable and multivariable calculus, basic linear algebra, signals and systems (continuous and discrete) and familiarity with basic deterministic system analysis; introductory probability is helpful but not strictly required.
Key Takeaways
- Model random signals and noise in communications, radar, and audio systems using discrete‑ and continuous‑time random processes.
- Analyze autocorrelation, cross‑correlation, and power spectral density to perform spectral analysis and understand FFT‑based techniques.
- Predict the response of linear systems to random inputs and design/assess linear filters from a statistical perspective.
- Apply foundational limit theorems (law of large numbers, central limit theorem) and transform methods (characteristic and moment‑generating functions) in engineering contexts.
- Use concepts of stationarity, ergodicity, and Gaussian processes to simplify analysis and support practical signal‑processing assumptions.
- Formulate and solve basic detection and estimation problems and quantify performance in terms of probabilities and moments.
Topics Covered
- 1. Review of Probability Concepts and Random Variables
- 2. Joint Distributions and Statistical Dependence
- 3. Conditional Probability, Expectation, and Moment Generating Functions
- 4. Limit Theorems and Convergence of Random Variables
- 5. Introduction to Random Processes; Discrete‑Time Models
- 6. Stationarity, Ergodicity, and Moments of Random Processes
- 7. Correlation Functions and Power Spectral Density
- 8. Linear Systems Excited by Random Processes
- 9. Gaussian and Markov Processes
- 10. Poisson Processes and Renewal Theory (selected topics)
- 11. Spectral Analysis and Estimation Techniques
- 12. Applications to Communications, Radar, and Audio/Speech Examples
- Appendices: Mathematical Tools and Tables
Languages, Platforms & Tools
How It Compares
Compared with Papoulis' Probability, Random Variables, and Stochastic Processes, Leon‑Garcia is more application‑oriented and accessible for engineers, while Papoulis is denser and more mathematically rigorous.
