5.2 Explaining notation
While we’ve introduced much of the notation used throughout the book in the previous chapters, we’ll be introducing more notation associated with BDL in the following chapters. As such, we’ve provided an overview of the notation here for reference:
μ: The mean. To make it easy to cross-reference our chapter with the original Probabilistic Backpropagation paper, this is represented as m when discussing PBP.
σ: The standard deviation.
σ2: The variance (meaning the square of the standard deviation). To make it easy to cross-reference our chapter with the paper, this is represented as v when discussing PBP.
x: A single vector input to our model. If considering multiple inputs, we’ll use X to represent a matrix comprising multiple vector inputs.
x: An approximation of our input x.
y: A single scalar target. When considering multiple targets, we’ll use y to represent a vector of multiple scalar targets.
ŷ:...
