Charles Margossian, computational mathematician, discusses the differences between MCMC and Variational Inference (VI). They explore beginner questions, the practical applications of Bayesian methods in pharmacometrics and epidemiology, and the challenges of fitting mechanistic models in drug absorption and effects. They also touch on nested Laplace approximations and the complexity of Bayesian methods and data analysis.

Charles Margossian, a research fellow at the Flatiron Institute, and Brian Ward, a core developer of Stan, dive into the future of the Stan programming language. They discuss recent innovations like the addition of tuples, which enhance data handling efficiency. The duo emphasizes the importance of improved error messages for beginners adjusting to Stan's complexities. They also highlight community engagement and the development of new samplers to enhance performance, paving the way for user-friendly features that make Bayesian statistics more accessible.