Karthik Bharath

Bayes robustness with Fisher-Rao metric.
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A Riemannian-geometric framework is proposed to assess sensitivity of Bayesian procedures to modeling
assumptions based on the nonparametric Fisher–Rao metric; assessments include local and global robustness to perturbations of the likelihood and prior, and identification of influential observations. An important feature of the approach is in the unification of the perturbation and the inference via intrinsic analysis on the space of probability densities under the same Riemannian metric which leads to geometrically calibrated influence measures. Utility of the framework in interesting applications involving generalized mixed-effects and directional data models will be demonstrated.


Tony O’Hagan

"What can we do with a model that's wrong?"

People use models in all fields of science, technology, management, etc. These can range from highly complex mathematical models based on systems of differential equations to relatively simple empirical, statistical, models. This talk is about the uncertainty in the predictions made by models. One aspect of this has come to be called Uncertainty Quantification (UQ), and is concerned with deriving the uncertainty in model outputs induced by uncertainty in the inputs. But there is another component of uncertainty that is much more important: all models are wrong. This talk is about just how badly misled we can be if we forget this fact.