Elementary Probability Theory with Stochastic Processes

Chapter 1 Set
1.1 Sample sets
1.2 Operations with sets
1.3 Various relations
1.4 Indicator
Exercises
Chapter 2 Probability
2.1 Examples of probability
2.2 Definition and illustrations
2.3 Deductions from the axioms
2.4 Independent events
2.5 Arithmetical density
Exercises
Chapter 3 Counting
3.1 Fundamental rule
3.2 Diverse ways of sampling
3.3 Allocation models; binomial coefficients
3.4 How to solve it
Exercises
Chapter 4 Random Variables
4.1 What is a random variable?
4.2 How do random variables come about?
4.3 Distribution and expectation
4.4 Integer-valued random variables
4.5 Random variables with densities
4.6 General case
Exercises
Appendix 1 Borel Fields and General Random Variables
Chapter 5 Conditioning and Independence
5.1 Examples of conditioning
5.2 Basic formulas
5.3 Sequential sampling
5.4 Pólya's urn scheme
5.5 Independence and relevance
5.6 Genetical models
Exercises
Chapter 6 Mean, Variance and Transforms
6.1 Basic properties of expectation
6.2 The density case
6.3 Multiplication theorem; variance and covariance
6.4 Multinomial distribution
6.5 Generating function and the like
Exercises
Chapter 7 Poisson and Normal Distributions
7.1 Models for Poisson distribution
7.2 Poisson process
7 .3 From binomial to normal
7.4 Normal distribution
7.5 Central limit theorem
7.6 Law of large numbers
Exercises
Appendix 2 Stirling's Formula and De Moivre-Laplace's Theorem
Chapter 8 From Random Walks to Markov Chains
8.1 Problems of the wanderer or gambler
8.2 Limiting schemes
8.3 Transition probabilities
8.4 Basic structure of Markov chains
8.5 Further developments
8.6 Steady state
8.7 Winding up (or down?)
Exercises
Appendix 3 Martingale
General References
Answers to Problems
Table 1 Values of the standard normal distribution function
Index