ABSTRACT
This chapter describes examples of the general situation where several random variables are observed. It uses the joint probability mass function (discrete case) and the joint density (continuous case) to compute probabilities involving the random variables. One attractive feature of the multinomial distribution is that the marginal distributions have familiar functional forms. A more intuitive way to obtain a marginal distribution relies on the previous knowledge of binomial distributions. The chapter illustrates the marginal probability density functions and conditional probability density functions by using several examples. It discusses contour graphs of bivariate normal distributions with different correlations and explains the simulation of bivariate normal measurements using examples.
