Probability and Random Processes for Electrical and Computer Engineers
With updates and enhancements to the incredibly successful first edition, Probability and Random Processes for Electrical and Computer Engineers, Second Edition retains the best aspects of the original but offers an even more potent introduction to probability and random variables and processes. Written in a clear, concise style that illustrates the subject’s relevance to a wide range of areas in engineering and physical and computer sciences, this text is organized into two parts. The first focuses on the probability model, random variables and transformations, and inequalities and limit theorems. The second deals with several types of random processes and queuing theory.
New or Updated for the Second Edition:
- A short new chapter on random vectors that adds some advanced new material and supports topics associated with discrete random processes
- Reorganized chapters that further clarify topics such as random processes (including Markov and Poisson) and analysis in the time and frequency domain
- A large collection of new MATLAB®-based problems and computer projects/assignments
Each Chapter Contains at Least Two Computer Assignments
Maintaining the simplified, intuitive style that proved effective the first time, this edition integrates corrections and improvements based on feedback from students and teachers. Focused on strengthening the reader’s grasp of underlying mathematical concepts, the book combines an abundance of practical applications, examples, and other tools to simplify unnecessarily difficult solutions to varying engineering problems in communications, signal processing, networks, and associated fields.
Why Read This Book
You will gain a clear, engineering-focused grounding in probability and stochastic processes that directly supports DSP, communications, radar, and statistical signal-processing tasks. The book balances rigorous theorems with practical examples so you can move from theory to analyzing real random signals and system performance.
Who Will Benefit
Electrical and computer engineers, graduate students, and practicing engineers with some math background who need to apply probability and random-process methods to DSP, communications, radar, or queuing problems.
Level: Intermediate — Prerequisites: Single-variable and multivariable calculus, basic linear algebra, and introductory deterministic signals and systems; elementary probability is helpful but not strictly required.
Key Takeaways
- Model random signals and noise using random variables and stochastic processes relevant to DSP and communications
- Analyze autocorrelation, power spectral density, and apply spectral methods (including FFT-based reasoning) to random signals
- Apply Gaussian-process and linear-system results to evaluate filter and estimator performance under stochastic inputs
- Use limit theorems and inequalities to justify approximations and assess estimator/test reliability in communications and radar
- Model and analyze common counting and queueing processes (Poisson, Markov, renewal) for network and system-level behavior
- Formulate and solve basic detection and estimation problems with probabilistic tools used in speech/audio and radar systems
Topics Covered
- 1. Probability Models and Axioms
- 2. Random Variables and Distributions
- 3. Functions of Random Variables and Transforms
- 4. Expectation, Conditional Expectation, and Inequalities
- 5. Limit Theorems and Convergence Concepts
- 6. Introduction to Random Processes and Stationarity
- 7. Autocorrelation, Cross-correlation, and Power Spectral Density
- 8. Linear Systems Driven by Random Inputs and Filtering
- 9. Gaussian Processes and Applications in Estimation
- 10. Poisson, Renewal, and Markov Processes
- 11. Queueing Theory and Applications to Networks
- 12. Selected Topics: Detection, Estimation, and Practical Examples
Languages, Platforms & Tools
How It Compares
Covers much the same engineering ground as Papoulis & Pillai but is more concise and application-oriented for ECE students; more accessible and example-driven than some mathematically dense alternatives like Grimmett & Stirzaker.
