Appendix A

Elements of Probability Theory

In this appendix, we report basic notions of probability theory and stochastic process theory. The material of this appendix is based on Feller (1957), on Appendix B.1 of Santi (2005), and on online resources such as Wikipedia and Wolfram MathWorld.

A.1 Basic Notions of Probability Theory

Definition A.1 (Sample space)
A sample space Ω is a set representing all possible outcomes of a certain random experiment. A sample space is discrete if it is composed of a finite number of elements (e.g., outcomes of a coin toss experiment), or of infinitely many elements that can be arranged into a simple sequence e1, e2, ….


Definition A.2 (Random variable
A random variable X is a function defined on a sample space. If the sample space on which X is defined is discrete, X is said to be a discrete random variable.

Examples of random variables are the number of heads in a sequence of k coin tosses (discrete random variable), the position of a certain particle in a physical system, the position of a node moving according to a certain mobility model at a certain instant of time, and so on.

Definition A.3 (Probability distribution)
Let X be a discrete random variable, and let Ω = {x1, x2, …, xj, …} be the set of possible values of X. The function


is called the probability distribution (also called probability mass function) of the random variable ...

Get Mobility Models for Next Generation Wireless Networks now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.