Sampling out of probability distributions

We have talked about generative probability distributions that model the process of data production. When we perform an experiment and obtain data, we are sampling out of the generative distribution. The true generative distribution is unknown, but by sampling out of it, we gain insights about the generative process. For example, if I measure the heights of a collection of humans, I learn something about the generative distribution just by investigating the samples out of it (the measured data).

Similarly, we can learn a lot about probability distributions, including model generative distributions, by sampling out of them directly using random number generation. In this section, we will learn about the techniques for doing so.