English: Left: a Bayesian neural network with two hidden layers, transforming a 3-dimensional input (bottom) into a two-dimensional output ${\displaystyle (y_{1},y_{2})}$ (top). Right: output probability density function ${\displaystyle p(y_{1},y_{2})}$ induced by the random weights of the network. Video: as the width of the network increases, output distribution simplifies, ultimately converging to a multivariate normal in the infinite width limit. Note: the video is an artistic depiction of the progression, and not an actual simulation.