Summary
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 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, Text Data for Trading – Sentiment Analysis, where we'll discuss natural language processing and topic modeling, and in Chapter 20, Autoencoders for Conditional Risk Factors and Asset Pricing, where we'll introduce variational autoencoders.
The next chapter introduces nonlinear, tree-based models, namely decision trees, and shows how to combine multiple models into an ensemble...
