  This study concerns the formulation and application of Bayesian optimal
experimental design to symbolic discovery, which is the inference from
observational data of predictive models taking general functional forms. We
apply constrained first-order methods to optimize an appropriate selection
criterion, using Hamiltonian Monte Carlo to sample from the prior. A step for
computing the predictive distribution, involving convolution, is computed via
either numerical integration, or via fast transform methods.
