Random variables are introduced in chapter 2 and

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: in the context of a finite, or countably infinite, set of possible outcomes. Notions of expectation (also known as mean), variance, hypothesis testing, parameter estimation, multiple random variables, and well known probability distributions–Poisson, geometric, and binomial, are covered. The Bernoulli process is considered–it provides a simple setting to discuss a long, even infinite, sequence of event times, and provides a tie between the binomial and geometric probability distributions. The focus shifts in Chapter 3 from discrete-type random variables to continuous-type random variables. The chapter takes advantage of many parallels and connections between discrete-type and continuous-type random variables. The most important well known continuous-type distributions are covered: uniform, exponential, and normal (also known as Gaussian). Poisson processes are introduced–they are continuous-time limits of the Bernoulli processes described in Chapter 2. Parameter estimation and binary hypothes...
View Full Document

This note was uploaded on 02/09/2014 for the course ISYE 2027 taught by Professor Zahrn during the Spring '08 term at Georgia Institute of Technology.

Ask a homework question - tutors are online