| Abstract: | Relative surprise inferences are based on how beliefs change from a priori to a posteriori. As they are based on the posterior distribution of the integrated likelihood, inferences of this type are invariant under relabellings of the parameter of interest. The authors demonstrate that these inferences possess a certain optimality property. Further, they develop computational techniques for implementing them, provided that algorithms are available to sample from the prior and posterior distributions. |
