Before discussing queuing analysis, it is necessary to introduce some concepts from probability theory and statistics. So this chapter introduces some of the basic probability concepts that are useful for understanding and analyzing queuing network models. Many additional, more complex probability densities are known to be useful for representing certain types of real world processes. The chapter starts with the ideas of random events and sample space in basic probability theory. It examines some discrete and some continuous probability distributions that will help to solidify the basic probability theory. The concept of random variables forms the foundation for the discussion of probability distributions and density functions. The chapter explores some methods for obtaining often used statistics about random variables using their distributions and densities. Jointly distributed random variables are represented with the following notation: Stated simply, joint random variables derive their output from a function whose domain is the set of outcomes for all of the individual random variable domains.