Bayesian polynomial neural networks and polynomial neural ordinary differential equations
Fig 14
For the Lorenz Attractor, we show the kernel density estimates for the posterior distributions of the polynomial coefficients obtained with a.) the Laplace Approximation, b.) Markov Chain Monte Carlo, and c.) Variational Inference. The true value of the coefficients is shown in the legend. The legend is shared for each of the columns.