So far, we have considered the case of pmfs/pdfs which consider only one variable, $X$. However, in many real-life experiments, we may want to consider more than one quantity. For example, we might want to consider the stock price of all stocks in a portfolio or all types of animals a neural network identifies in a picture. To talk about these experiments effectively, we must use multivariate distributions, which measure the probability of more than one random variable. This first section will define what these distributions are.