This abstract is about probabilistic programming, which is a method of programming statistical models and machine learning models. By combining higher-order functions, we can specify increasingly complex models. This abstract focuses on stochastic memoization, a higher-order method that is simple and useful in practice, but semantically elusive, particularly regarding dataflow transformations.