O'Reilly logo

Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach by Hong Xie, Chaoqun Ma, Guojun Gan

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

CHAPTER 11

EVENTS AND RANDOM VARIABLES

Samples, events, and random variables are the fundamental concepts in probability theory. In this chapter, we shall introduce the definitions of these concepts based on measure theory.

11.1 Basic Concepts and Facts

Definition 11.1 (Sample Space, Sample Point, and Event). Let (Ω, , P) be a probability space (see Definition 2.12). The set Ω is called the sample space, a point in Ω is called a sample point, and a subset of is called an event.

Definition 11.2 (Discrete Probability Space). Let Ω = {ω1, ω2,…} be a finite or countably infinite set. Let P be a set function defined as

equation

and

equation

where p1, p2,… are nonnegative numbers whose sum is 1. Then P is a probability measure on 2Ω, which is the collection of all subsets of Ω. The probability space (Ω, 2Ω, P) is called a discrete probability space.

Definition 11.3 (Almost Surely). Let be a probability space. A statement about outcomes is said to be true almost surely (a.s.), or with probability 1, if the set ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required