pbdl-book/bayesian-intro.md

Introduction to Posterior Inference

more complex models -> fit data better , but overfit & dont generalize -> posterior (distrib) inference

std NNs do MLE for weights “theoretically justifiable” ie correct thing to do -> “posterior inference” extremely difficult

useful for uncertainty! esp interesting in phys setting

first example here with airfoils, extension from {doc}supervised-airfoils