Experimental Design to Maximize Information

This paper will consider different methods to measure the gain of information that an experiment provides on parameters of a statistical model. The approach we follow is Bayesian and relies on the assumption that information about model parameters is represented by their probability distribution so that a measure of information is any summary of the probability distributions satisfying some sensible assumptions. Robustness issues will be considered and investigated in some examples using a new family of information measures which have the log-score and the quadratic score as special cases.