Date: 2019-11-01

Time: 16:00-17:00

Location: BURN 1104

Abstract:

The work is motivated by the inflexibility of Bayesian modeling; in that only parameters of probability models are required to be connected with data. The idea is to generalize this by allowing arbitrary unknowns to be connected with data via loss functions. An updating process is then detailed which can be viewed as arising in at least a couple of ways - one being purely axiomatically driven. The further exploration of replacing probability model based approaches to inference with loss functions is ongoing. Joint work with Chris Holmes, Pier Giovanni Bissiri and Simon Lyddon.

Speaker

Stephen G. Walker is a Professor in the Department of Mathematics, Department of Statistics and Data Sciences at the University of Texas at Austin. He joined the University of Texas at Austin in 2013, having previously held positions at the University of Kent, Bath and Imperial College, all in the UK. He is currently Executive Editor for Journal of Statistical Planning and Inference, and Associate Editor for Journal of the American Statistical Association, having previously been Associate Editor for Scandinavian Journal of Statistics, Statistica Sinica, and Annals of Statistics.