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

Description

This market leader is written as an elementary introduction to the mathematical theory of probability for students 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 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 conclude each chapter. In addition; many new 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 students 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.

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  • Preface

    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 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
    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 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
    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
    5.4.1 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
    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 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
    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
    8.6 Bounding the Error Probability When Approximating a Sum of Independent Bernoulli Random Variables by a Poisson
    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
    Theoretical Exercises and Problems
    Self-Test Problems and Exercises
    References

    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
    Summary
    Problems
    Self-Test Problems and Exercises

    References
    Appendix A Answers to Selected Problems
    Appendix B Solutions to Self-Test Problems and Exercises
    Index
  • Citation

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