ABSTRACT
In this chapter, the authors start their actual technical discussion by introducing some of the probability concepts. They begin this presentation by talking about discrete probability distributions. Later, They generalize the technical discussion to include probability distributions over a continuous-variable sample space. They also introduces the Central Limit Theorem, both for continuous and for discrete probability distributions. Narrowly stated, the Central Limit Theorem requires the sum of n outcomes of an event to approach a normal distribution as n approaches infinity, provided only that the event outcome has a clearly defined expectation and variance. When treating multiple variables, they will need to introduce the concepts of dependence, independence, covariance, and correlation. Characteristic functions occur frequently in statistical literature. In other words, the characteristic or moment-generating function gives immediate access to the moments of the associated distribution.
