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.