  # First Course in Probability, A, 10th edition

• Sheldon Ross

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## Overview

### Hallmark features of this title

• Analysis is unique to the text and elegantly designed. Examples include the knockout tournament and multiple players gambling ruin problem, along with results concerning the sum of uniform and the sum of geometric random variables.
• Intuitive explanations are supported with an abundance of examples to give readers a thorough introduction to both the theory and applications of probability.
• 3 sets of exercises are given at the end of each chapter: Problems, Theoretical Exercises and Self-Test Problems and Exercises.
• Self-Test Problems and Exercises include complete solutions in the appendix, allowing students to test their comprehension and study for exams.

ISBN-13: 9780138027889

Category: Probability & Statistics

### 1. Combinatorial Analysis

• 1.1 Introduction
• 1.2 The Basic Principle of Counting
• 1.3 Permutations
• 1.4 Combinations
• 1.5 Multinomial Coefficients
• 1.6 The Number of Integer Solutions of Equations
• Summary
• Problems
• Theoretical Exercises
• Self-Test Problems and Exercises

### 2. Axioms of Probability

• 2.1 Introduction
• 2.2 Sample Space and Events
• 2.3 Axioms of Probability
• 2.4 Some Simple Propositions
• 2.5 Sample Spaces Having Equally Likely Outcomes
• 2.6 Probability as a Continuous Set Function
• 2.7 Probability as a Measure of Belief
• Summary
• Problems
• Theoretical Exercises
• Self-Test Problems and Exercises

### 3. Conditional Probability and Inference

• 3.1 Introduction
• 3.2 Conditional Probabilities
• 3.3 Bayes’s Formula
• 3.4 Independent Events
• 3.5 P(·|F) Is a Probability
• Summary
• Problems
• Theoretical Exercises
• Self-Test Problems and Exercises

### 4. Random Variables

• 4.1 Random Variables
• 4.2 Discrete Random Variables
• 4.3 Expected Value
• 4.4 Expectation of a Function of a Random Variable
• 4.5 Variance
• 4.6 The Bernoulli and Binomial Random Variables
• 4.7 The Poisson Random Variable
• 4.8 Other Discrete Probability Distributions
• 4.9 Expected Value of Sums of Random Variables
• 4.10 Properties of the Cumulative Distribution Function
• Summary
• Problems
• Theoretical Exercises
• Self-Test Problems and Exercises

### 5. Continuous Random Variables

• 5.1 Introduction
• 5.2 Expectation and Variance of Continuous Random Variables
• 5.3 The Uniform Random Variable
• 5.4 Normal Random Variables
• 5.5 Exponential Random Variables
• 5.6 Other Continuous Distributions
• 5.7 The Distribution of a Function of a Random Variable
• Summary
• Problems
• Theoretical Exercises
• Self-Test Problems and Exercises

### 6. Jointly Distributed Random Variables

• 6.1 Joint Distribution Functions
• 6.2 Independent Random Variables
• 6.3 Sums of Independent Random Variables
• 6.4 Conditional Distributions: Discrete Case
• 6.5 Conditional Distributions: Continuous Case
• 6.6 Order Statistics
• 6.7 Joint Probability Distribution of Functions of Random Variables
• 6.8 Exchangeable Random Variables
• Summary
• Problems
• Theoretical Exercises
• Self-Test Problems and Exercises

### 7. Properties of Expectation

• 7.1 Introduction
• 7.2 Expectation of Sums of Random Variables
• 7.3 Moments of the Number of Events that Occur
• 7.4 Covariance, Variance of Sums, and Correlations
• 7.5 Conditional Expectation
• 7.6 Conditional Expectation and Prediction
• 7.7 Moment Generating Functions
• 7.8 Additional Properties of Normal Random Variables
• 7.9 General Definition of Expectation
• Summary
• Problems
• Theoretical Exercises
• Self-Test Problems and Exercises

### 8. Limit Theorems

• 8.1 Introduction
• 8.2 Chebyshev’s Inequality and the Weak Law of Large Numbers
• 8.3 The Central Limit Theorem
• 8.4 The Strong Law of Large Numbers
• 8.5 Other Inequalities and a Poisson Limit Result
• 8.6 Bounding the Error Probability When Approximating a Sum of Independent Bernoulli Random Variables by a Poisson Random Variable
• 8.7 The Lorenz Curve
• Summary
• Problems
• Theoretical Exercises
• Self-Test Problems and Exercises

### 9. Additional Topics in Probability

• 9.1 The Poisson Process
• 9.2 Markov Chains
• 9.3 Surprise, Uncertainty, and Entropy
• 9.4 Coding Theory and Entropy
• Summary
• Problems and Theoretical Exercises
• Self-Test Problems and Exercises

### 10. Simulation

• 10.1 Introduction
• 10.2 General Techniques for Simulating Continuous Random Variables
• 10.3 Simulating from Discrete Distributions
• 10.4 Variance Reduction Techniques
• Summary
• Problems
• Self-Test Problems and Exercises

#### Common Continuous Distributions

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