Explore Bayesian inference through the lens of program verification in this 45-minute talk by Joost-Pieter Katoen from RWTH Aachen University. Discover how weakest precondition reasoning can be applied to exact inference in Bayesian networks and learn about automated techniques for determining exact expected sampling times. Gain insights into the practical implications of these methods for deciding the appropriateness of sampling-based approaches for given Bayesian networks. Delve into topics such as probabilistic graphical models, weakest pre-expectations, and Bayesian networks as programs. Examine real-world applications, including student mood prediction and printer troubleshooting in Windows 95. This presentation, part of a workshop on combining logic and learning, offers a unique perspective on the intersection of formal methods and statistical approaches in understanding complex systems.