Bayesian Inference for Molecular Simulations

This lecture by the instructor focuses on the application of Bayesian Inference for Molecular Simulations, specifically in the context of water transport in carbon nanotubes. The content covers the use of data as models, hierarchical Bayesian inference, and model selection for coarse-grained water models. Various water models and their structures are discussed, along with the challenges and benefits of Bayesian inference compared to machine learning. The lecture emphasizes the importance of incorporating prior knowledge, quantifying uncertainty, and addressing ill-posed problems in computational modeling.