Variational Bayes

Variational Bayes is a method used to approximate complex Bayesian models when exact calculations are too difficult or computationally intensive. It works by transforming the problem into an easier one: instead of directly calculating the true probability distributions, it finds simpler, approximate distributions that are close to the real ones. The process involves tweaking these simpler functions to minimize the difference from the true distributions, allowing us to make meaningful inferences about the data efficiently. Essentially, Variational Bayes provides a practical way to perform Bayesian analysis when exact solutions are impractical.