Schaum's Outline of Probability, Random Variables, and Random Processes, 3rd Edition
Tough Test Questions? Missed Lectures? Not Enough Time?
Fortunately, there's Schaum's. This all-in-one-package includes more than 400 fully solved problems, examples, and practice exercises to sharpen your problem-solving skills. Plus, you will have access to 20 detailed videos featuring instructors who explain the most commonly tested problems--it's just like having your own virtual tutor! You'll find everything you need to build confidence, skills, and knowledge for the highest score possible.
More than 40 million students have trusted Schaum's to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test your skills.
This Schaum's Outline gives you
- 405 fully solved problems
- Clear, concise explanations of all probability, variables, and processes concepts
- Support for all the major textbooks in the subject areas
Fully compatible with your classroom text, Schaum's highlights all the important facts you need to know. Use Schaum’s to shorten your study time--and get your best test scores!
Schaum's Outlines--Problem Solved.
Why Read This Book
You should read this if you want a focused, practice-oriented reference to probability and stochastic processes with hundreds of solved problems that build intuition for DSP applications. It’s especially useful when you need quick worked examples to reinforce theory used in spectral analysis, filtering, and communications.
Who Will Benefit
Undergraduate or graduate engineering students and practicing DSP/communications engineers who need a problem-driven refresher or homework/practice resource on probability and random processes.
Level: Intermediate — Prerequisites: Single-variable calculus, basic linear algebra, and elementary familiarity with signals and systems or basic probability concepts.
Key Takeaways
- Compute probabilities and distributions for common continuous and discrete random variables and combinations
- Manipulate joint distributions, conditional expectations, and moments for multivariate random vectors
- Use characteristic functions, moment-generating functions, and transform methods to analyze distributions and convergence
- Analyze random processes: stationarity, ergodicity, autocorrelation, covariance, and power spectral density
- Model and work with Gaussian, Poisson, and Markov processes and evaluate responses of LTI systems driven by random inputs
- Apply limit theorems (law of large numbers, central limit theorem) and basic estimation ideas used in statistical signal processing
Topics Covered
- Basic Probability Concepts
- Discrete and Continuous Random Variables
- Functions of Random Variables and Transform Methods
- Multiple Random Variables and Joint Distributions
- Expectation, Moments, and Conditional Expectation
- Characteristic Functions and Limit Theorems
- Introduction to Random Processes: Definitions and Properties
- Stationarity, Ergodicity, Autocorrelation and Covariance
- Power Spectral Density and Spectral Analysis
- Special Processes: Gaussian, Poisson, and Markov Processes
- Linear Systems Excited by Random Processes
- Basic Statistical Topics: Estimation and Detection (intro) & Worked Problems
How It Compares
Covers similar practical ground to Leon-Garcia's Probability and Random Processes for Engineers but is far more problem- and example-focused; less theoretical and rigorous than Papoulis' classic text.
