Probabilistic Deep Learning

7 Bayesian learning

This chapter covers

• Identifying extrapolation as the Achilles heel of DL
• A gentle introduction to Bayesian modeling
• The concept of model uncertainty, which is called epistemic uncertainty
• The Bayesian approach as a state-of-the-art method to dealing with parameter uncertainty

This chapter introduces Bayesian models. Besides the likelihood approach, the Bayesian approach is the most important method to fit the parameters of a probabilistic model and to estimate the associated parameter uncertainty. The Bayesian modeling approach incorporates an additional kind of uncertainty, called epistemic uncertainty. You will see that ...