Author 
Yates, Roy D.,

Subject 
Probabilities.


Stochastic processes.

Alt Name 
Goodman, David J., 1939

Description 
1 online resource (xvi, 496 pages) 
Edition 
Third edition. 
Bibliography Note 
Includes bibliographical references and index. 
Note 
Machine generated contents note: Chapter 1. Experiments, Models, and Probabilities Chapter 2. Discrete Random Variables Chapter 3. Continuous Random Variables Chapter 4. Pairs of Random Variables Chapter 5. Random Vectors Chapter 6. Sums of Random Variables Chapter 7. Parameter Estimation Using the Sample Mean Chapter 8. Hypothesis Testing Chapter 9. Estimation of a Random Variable Chapter 10. Stochastic Processes Chapter 11. Random Signal Processing Chapter 12. Markov Chains. 
Contents 
Cover; Title Page; Copyright; Features of this Text; Who will benefit from using this text?; What's New?; Notable Features; Instructor Support; Preface; Welcome to the third edition; How the book is organized; What is distinctive about this book?; Further Reading; Acknowledgments; A Message to Students from the Authors; Contents; Chapter 1: Experiments, Models, and Probabilities; Getting Started with Probability; 1.1 Set Theory; 1.2 Applying Set Theory to Probability; 1.3 Probability Axioms; 1.4 Conditional Probability; 1.5 Partitions and the Law of Total Probability; 1.6 Independence. 

1.7 MatlabProblems; Chapter 2: Sequential Experiments; 2.1 Tree Diagrams; 2.2 Counting Methods; 2.3 Independent Trials; 2.4 Reliability Analysis; 2.5 Matlab; Problems; Chapter 3: Discrete Random Variables; 3.1 Definitions; 3.2 Probability Mass Function; 3.3 Families of Discrete Random Variables; 3.4 Cumulative Distribution Function (CDF); 3.5 Averages and Expected Value; 3.6 Functions of a Random Variable; 3.7 Expected Value of a Derived Random Variable; 3.8 Variance and Standard Deviation; 3.9 Matlab; Problems; Chapter 4: Continuous Random Variables; 4.1 Continuous Sample Space. 

4.2 The Cumulative Distribution Function4.3 Probability Density Function; 4.4 Expected Values; 4.5 Families of Continuous Random Variables; 4.6 Gaussian Random Variables; 4.7 Delta Functions, Mixed Random Variables; 4.8 Matlab; Problems; Chapter 5: Multiple Random Variables; 5.1 Joint Cumulative Distribution Function; 5.2 Joint Probability Mass Function; 5.3 Marginal PMF; 5.4 Joint Probability Density Function; 5.5 Marginal PDF; 5.6 Independent Random Variables; 5.7 Expected Value of a Function of Two Random Variables; 5.8 Covariance, Correlation and Independence. 

5.9 Bivariate Gaussian Random Variables5.10 Multivariate Probability Models; 5.11 Matlab; Problems; Chapter 6: Probability Models of Derived Random Variables; 6.1 PMF of a Function of Two Discrete Random Variables; 6.2 Functions Yielding Continuous Random Variables; 6.3 Functions Yielding Discrete or Mixed Random Variables; 6.4 Continuous Functions of Two Continuous Random Variables; 6.5 PDF of the Sum of Two Random Variables; 6.6 Matlab; Problems; Chapter 7: Conditional Probability Models; 7.1 Conditioning a Random Variable by an Event; 7.2 Conditional Expected Value Given an Event. 

7.3 Conditioning Two Random Variables by an Event7.4 Conditioning by a Random Variable; 7.5 Conditional Expected Value Given a Random Variable; 7.6 Bivariate Gaussian Random Variables: Conditional PDFs; 7.7 Matlab; Problems; Chapter 8: Random Vectors; 8.1 Vector Notation; 8.2 Independent Random Variables and Random Vectors; 8.3 Functions of Random Vectors; 8.4 Expected Value Vector and Correlation Matrix; 8.5 Gaussian Random Vectors; 8.6 Matlab; Problems; Chapter 9: Sums of Random Variables; 9.1 Expected Values of Sums; 9.2 Moment Generating Functions. 
Summary 
"In Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, readers are able to grasp the concepts of probability and stochastic processes, and apply these in professional engineering practice. The 3rd edition also includes quiz solutions within the appendix of the text. The resource presents concepts clearly as a sequence of building blocks identified as an axiom, definition or theorem. This approach allows for a better understanding of the material, which can be utilized in solving practical problems" Provided by publisher. 
Note 
Description based on online resource; title from digital title page (viewed on January 14, 2019). 
ISBN 
9781118804384 electronic book 

1118804384 electronic book 

9781118324561 paperback 

1118324560 paperback 
OCLC # 
864753147 
Additional Format 
Print version: Yates, Roy D. Probability and stochastic processes. Third edition. Hoboken, NJ : John Wiley & Sons, [2014] 9781118324561 (DLC) 2013047063 
