A rather large number of LoFi points is typically used in 1DHR models to obtain a smooth PES. For later application to a N DHR problem, this becomes prohibitive. For the more expensive HiFi points it is crucial to save as much computational time as possible by choosing the location of the computations in an optimal way. Bayesian prediction methods can provide such a way. Being known for decades, they have recently become popular for estimating parameters in complicated unstructured models like neural networks. They are also useful in many other more classic applications when it is necessary to sample spaces efficiently. Here, they allow improved decision making about additional samples.