Bayesian experimental design

Bayesian experimental design differs from the classical approach in that the purpose of the experiment is explicitly represented in the form of a loss function. Different loss functions imply different ways to optimise the design. Designing to best estimate model parameters leads to Bayes a-optimal designs, whereas designing to maximise the information gained (measured by Kullback–Leibler divergence) leads to Bayes d-optimal designs.