Variational inference

Variational inference is a method used in statistics and machine learning to estimate complex probability models. It works by approximating an intractable (very difficult to compute) probability distribution with a simpler, manageable one. Think of it like trying to find a close, easy-to-understand model that captures the essentials of a complicated system. The goal is to "fit" this simpler model to the data by adjusting its parameters, making the approximation as accurate as possible. This process allows us to make predictions and draw insights from complex models more efficiently than trying to compute the exact probabilities directly.