A First Course in Probability – Sheldon M. Ross – 5th Edition


This market leader is written as an elementary introduction to the mathematical theory of probability for in mathematics; engineering; and the sciences who possess the prerequisite knowledge of elementary calculus.

A major thrust of the Fifth Edition has been to make the book more accessible to today’s students. The exercise sets have been revised to include more simple; mechanical and a new section of Self-Test Problems with fully worked out solutions conclude each chapter. In addition; many new applications have been added to demonstrate the importance of probability in real situations.

A diskette; referenced in text and packaged with each copy of the book; provides an easy to use tool for to derive probabilities for binomial; Poisson; and normal random variables; illustrate and explore the central limit theorem; work with the strong law of large numbers; and more.

View more

Table of Contents


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
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
Theoretical Exercises
Self-Test Problems and Exercises

3 Conditional Probability and Independence
3.1 Introduction
3.2 Conditional Probabilities
3.3 Bayes' Formula
3.4 Independent Events
3.5 P(-[middle dot]F) is a Probability
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 Expectatio of a Function of a Random Variable
4.5 Variance
4.6 The Bernoulli and Binomial Random Variables
4.6.1 Properties of Binomial Random Variables
4.6.2 Computing the Binomial Distribution Function
4.7 The Poisson Random Variable
4.7.1 Computing the Poisson Distribution Function
4.8 Other Discrete Probability Distribution
4.8.1 The Geometric Random Variable
4.8.2 The Negative Binomial Random Variable
4.8.3 The Hypergeometric Random Variable
4.8.4 The Zeta (or Zipf) distribution
4.9 Properties of the Cumulative Distribution Function
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
5.4.1 The Normal Approximation to the Binomial Distribution
5.5 Exponential Random Variables
5.5.1 Hazard Rate Functions
5.6 Other Continuous Distributions
5.6.1 The Gamma Distribution
5.6.2 The Weibull Distribution
5.6.3 The Cauchy Distribution
5.6.4 The Beta Distribution
5.7 The Distribution of a Function of a Random Variable
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
Theoretical Exercises
Self-Test Problem and Exercises

7 Properties of Expectation
7.1 Introduction
7.2 Expectation of Sums of Random Variables
7.2.1 Obtaining Bounds from Expectations via the Probabilistic Method
7.2.2 The Maximum-Minimums Identity
7.3 Covariance, Variance of Sums, and Correlations
7.4 Conditional Expectation
7.4.1 Definitions
7.4.2 Computing Expectations by Conditioning
7.4.3 Computing Probabilities by Conditioning
7.4.4 Conditional Variance
7.5 Conditional Expectation and Prediction
7.6 Moment Generating Functions
7.6.1 Joint Moment Generating Functions
7.7 Additional Properties of Normal Random Variables
7.7.1 The Multivariate Normal Distribution
7.7.2 The Joint Distribution of the Sample Mean and Sample Variance
7.8 General Definition of Expectation
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
8.6 Bounding the Error Probability When Approximating a Sum of Independent Bernoulli Random Variables by a Poisson
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
Theoretical Exercises and Problems
Self-Test Problems and Exercises

10 Simulation
10.1 Introduction
10.2 General Techniques for Simulating Continuous Random Variables
10.2.1 The Inverse Transformation Method
10.2.2 The Rejection Method
10.3 Simulating from Discrete Distributions
10.4 Variance Reduction Techniques
10.4.1 Use of Antithetic Variables
10.4.2 Variance Reduction by Conditioning
10.4.3 Control Variates
Self-Test Problems and Exercises

Appendix A Answers to Selected Problems
Appendix B Solutions to Self-Test Problems and Exercises

Leave us a commentNo Comments

Inline Feedbacks
View all comments
Would love your thoughts, please comment.x