Introduction to Bayesian ML/DL, with Application to Parameter Inference of Coupled Non-linear ODEs – Part 2
B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofIn this talk, the speaker will present introductory materials about Bayesian Machine Learning. Abstract The problem of approximating the posterior distribution (or density estimation in general) is a crucial problem in Bayesian statistics, in which intractable integrals often become the computational bottleneck. MCMC sampling is the most widely used family of algorithms for approximating posteriors. …