# A First Course in Probability, Global Edition (10e)

##### Description

*This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States.*

For upper-level to graduate courses in Probability or Probability and Statistics, for majors in mathematics, statistics, engineering, and the sciences.

Explores both the mathematics and the many potential applications of probability theory

**A First Course in Probability** offers an elementary introduction to the theory of probability for students in mathematics, statistics, engineering, and the sciences. Through clear and intuitive explanations, it attempts to present not only the mathematics of probability theory, but also the many diverse possible applications of this subject through numerous examples. The 10th Edition includes many new and updated problems, exercises, and text material chosen both for inherent interest and for use in building student intuition about probability.

##### Table of contents

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 INDEPENDENCE

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 394

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

Answers to Selected Problems

Solutions to Self-Test Problems and Exercises

Index

Common Discrete Distributions

Common Continuous Distributions

##### Features & benefits

- 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.
- New - Examples such as Example 4n of Chapter 3, which deals with computing NCAA basketball tournament win probabilities, and Example 5b of Chapter 4, which introduces the friendship paradox.
- New - Streamlined exposition focuses on clarity and deeper understanding.
- Many new and updated problems and exercises.
- New - Material on the Pareto distribution (introduced in Section 5.6.5), on Poisson limit results (in Section 8.5), and on the Lorenz curve (in Section 8.7).
- Three 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.