This preview shows pages 1–4. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: Discrete Probabilities Marius Ionescu Fall 2005 Random Variables and Sample Spaces We represent the outcome of the experiment by a capital Roman letter, such as X , called a random variable . The sample space of the experiment is the set of all possible outcomes. If the sample space is either finite or countably infinite, the random variable is said to be discrete . The elemnts of a sample space are called outcome. A subset of the sample space is called an event. 1 Distribution Functions Let X be a random variable which denotes the value of the outcome of a certain experiment, and assume that this experiment has only finitely many possible outcomes. Let be the sample space of the experiment (i.e., the set of all possible values of X , or equivalently, the set of all possible outcomes of the experiment.) A distribution function for X is a realvalued function m whose domain is and which satisfies: 1. m ( ) , for all , and 2....
View Full
Document
 Fall '05
 Ionescu
 Probability

Click to edit the document details