included BNN chapter

bayesian-intro.md

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+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`
+