4.5 Summary
In this chapter, we were introduced to the fundamental concepts that we’ll need to progress through the book and learn how to implement and use BNNs. Most crucially, we learned about the ideal BNN, which introduced us to the core ideas underlying BDL, and the computational difficulties of achieving this in practice. We also covered the fundamental practical methods used in BDL, giving us a grounding in the concepts that allow us to implement computationally tractable BNNs.
The chapter also introduced the concept of uncertainty sources, describing the difference between data and model uncertainty, how these contribute to total uncertainty, and how we can estimate the contributions of different types of uncertainty with various models. We also introduced one of the most fundamental components in probabilistic inference – the likelihood function – and learned about how it can help us to train better principled and better calibrated models. Lastly, we were...
