Probability and Measure

 

There are plenty of standard texts introducing measure-theoretic probability. Billingsley is easily the most cited. Like many books, it assuming no prior knowledge of measure theory, teaching it alongside probability theory. However, its approach is slightly different from other books, such as Williams (1991) or Resnick (1999). While those books teach measure theoretic concepts as they are needed, Billingsley takes time off and devotes the entire second and third chapters exclusively to measure theory. Then, he applies the measure theory to a formal development of probability.

Standard topics are covered, including convergence theorems, characteristic functions, Radon-Nikodkym derivatives, conditional expectations, martingales and Brownian motion. These are the essentials for anyone who wants to go on and study stochastic calculus.

Billingsley is a very sophisticated, formal book. It is targeted more towards mathematicians than practitioners. If you are new to measure-theoretic probability, you might select a more accessible book. As a reference or a second book on the topic, I highly recommend Billingsley.

Contents

1. Probability

Borel's Normal Number Theorem

Probability Measures

Existence and Extension

Denumerable Probabilities

Simple Random Variables

The Law of Large Numbers

Gambling Systems

Markov Chains

Large Deviations and the Law of the Iterated Logarithm

2. Measure

General Measures

Outer Measure

Measures in Euclidean Space

Measurable Functions and Mappings

Distribution Functions

3. Integration

The Integral

Properties of the Integral

The Integral with Respect to Lebesgue Measure

Product Measure and Fubini's Theorem

The Lp Spaces

4. Random Variables and Expected Values

Random Variables and Distributions

Expected Values

Sums of Independent Random Variables

The Poisson Process

The Ergodic Theorem

5. Convergence of Distributions

Weak Convergence

Characteristic Functions

The Central Limit Theorem

Infinitely Divisible Distributions

Limit Theorems in Rk

The Method of Moments

6. Derivatives and Conditional Probability

Derivatives on the Line

The Radon-Nikodym Theorem

Conditional Probability

Conditional Expectation

Martingales

7. Stochastic Processes

Kolmogorov's Existence Theorem

Brownian Motion

Nondenumerable Probabilities

For related books, see sections:

Mathematics - Measure Theory

Mathematics - Probability

Mathematics - Stochastic Calculus

Financial Engineering - Numerical Methods

Financial Engineering - Intermediate Theory

Financial Engineering - Advanced Theory

 

 

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