In this lecture, we review basic probability space concepts from the previous lecture. We then go on to discuss the common probabilistic models that we will use in stochastic simulation (e.g., uniform, triangular, normal, exponential, Weibull, Erlang, Poisson, etc.). Basic background on the structure of each distribution is given as well as practical reasons why one distribution might be picked over another.