Neural networks define a function f : X → Y, parameterized by a set of weights and biases ω = {W l , b l } L l=1 , where L denotes the number of layers. Unlike conventional neural networks, which treat their parameters as point estimates, BNNs treat their parameters as random variables and place a prior distribution p(ω) over the network's parameters . Typically, the prior is a zero mean Gaussian with diagonal covariance, N ∼ (0, I).