In this chapter, we explored Bayesian approaches to machine learning. We saw that they have several advantages, including the ability to encode prior knowledge or opinions, deeper insights into the uncertainty surrounding model estimates and predictions, and the suitability for online learning, where each training sample incrementally impacts the model's prediction.
We learned to apply the Bayesian workflow from model specification to estimation, diagnostics, and prediction using PyMC3 and explored several relevant applications. We will encounter more Bayesian models in Chapter 14, Topic Modeling and in Chapter 19, Autoencoders and Generative Adversarial Nets, we will introduce variational autoencoders.
The next two chapter introduce tree-based, non-linear ensemble models, namely random forests and gradient boosting machines.