4.3 BDL fundamentals
Throughout the rest of the book, we will introduce a range of methods necessary to make BDL possible. There are a number of common themes present through these methods. We’ll cover these here, so that we have a good understanding of these concepts when we encounter them later on.
These concepts include the following:
Gaussian assumptions: With many BDL methods, we use Gaussian assumptions to make things computationally tractable
Uncertainty sources: We’ll take a look at the different sources of uncertainty, and how we can determine the contributions of these sources for some BDL methods
Likelihoods: We were introduced to likelihoods in Chapter 2, Fundamentals of Bayesian Inference, and here we’ll learn more about the importance of likelihood as a metric for evaluating the calibration of probabilistic models
Let’s look at each of these in the following subsections.
4.3.1 Gaussian assumptions
In the ideal case described previously...
