Book:Geoffrey Grimmett/Probability: An Introduction

Subject Matter

 * Probability Theory

Contents

 * Preface

A. BASIC PROBABILITY

 * 1 Events and probabilities
 * 1.1 Experiments with chance
 * 1.2 Outcomes and events
 * 1.3 Probabilities
 * 1.4 Probability spaces
 * 1.5 Discrete sample spaces
 * 1.6 Conditional probabilities
 * 1.7 Independent events
 * 1.8 The partition theorem
 * 1.9 Probability measures are continuous
 * 1.10 Worked problems
 * 1.11 Problems


 * 2 Discrete random variables
 * 2.1 Probability mass functions
 * 2.2 Examples
 * 2.3 Functions of discrete random variables
 * 2.4 Expectation
 * 2.5 Conditional expectation and the partition theorem
 * 2.6 Problems


 * 3 Multivariate discrete distributions and independence
 * 3.1 Bivariate discrete distributions
 * 3.2 Expectation in the multivariate case
 * 3.3 Independence of discrete random variables
 * 3.4 Sums of random variables


 * 4 Probability generating functions
 * 4.1 Generating functions
 * 4.2 Integer-valued random variables
 * 4.3 Moments
 * 4.4 Sums of independent random variables
 * 4.5 Problems


 * 5 Distribution functions and density functions
 * 5.1 Distribution functions
 * 5.2 Examples of distribution functions
 * 5.3 Continuous random variables
 * 5.4 Some common density functions
 * 5.5 Functions of random variables
 * 5.6 Expectations of continuous random variables
 * 5.7 Problems

B. FURTHER PROBABILITY

 * 6 Multivariate distributions and independence
 * 6.1 Random vectors and independence
 * 6.2 Joint density functions
 * 6.3 Marginal density functions and independence
 * 6.4 Sums of continuous random variables
 * 6.5 Changes of variables
 * 6.6 Conditional density functions
 * 6.7 Expectations of continuous random variables
 * 6.8 Conditional expectation and the bivariate normal distribution
 * 6.9 Problems


 * 7 Moments, and moment generating functions
 * 7.1 A general note
 * 7.2 Moments
 * 7.3 Variance and covariance
 * 7.4 Moment generating functions
 * 7.5 Characteristic functions
 * 7.6 Problems


 * 8 The two main limit theorems
 * 8.1 The law of averages
 * 8.2 Chebyshev's inequality and the weak law
 * 8.3 The central limit theorem
 * 8.4 Convergence in distribution, and characteristic functions
 * 8.5 Problems

C. RANDOM PROCESSES

 * 9 Branching processes
 * 9.1 Random processes
 * 9.2 A model for population growth
 * 9.3 The generating-function method
 * 9.4 An example
 * 9.5 The probability of extinction
 * 9.6 Problems


 * 10 Random walks
 * 10.1 One-dimensional random walks
 * 10.2 Transition probabilities
 * 10.3 Recurrence and transience in random walks
 * 10.4 The Gambler's Ruin problem
 * 10.5 Problems


 * 11 Random processes in continuous time
 * 11.1 Life at a telephone exchange
 * 11.2 Poisson processes
 * 11.3 Inter-arrival times and the exponential distribution
 * 11.4 Population growth and the simple birth process
 * 11.5 Birth and death processes
 * 11.6 A simple queueing model
 * 11.7 Problems


 * Appendix: Difference equations


 * Answers to exercises


 * Remarks on the problems


 * Reading list


 * Index



Source work progress
* : $\S 5.1$: Distribution Functions
 * Redoing from start: examples and exercises to be covered


 * : $1$: Events and probabilities: $1.4$: Probability spaces: Exercise $7$