STOR-i Seminar: Professor Kerrie Mengersen
"Let's talk about priors"
Priors are integral to Bayesian analysis. Such priors are typically constructed to enable parameter estimation, facilitate model choice, provide spatial and/or temporal smoothing, reflect complex structure in a system, and so on. In all of these cases, the posterior probability distributions are conditioned on the data. In this presentation, we examine a larger class of problems in which the priors are jointly used to inform about the parameters and/or model as well as the data. We consider this both in a general setup and in the context of a real world case study involving transferability, that is, the task of developing a model based on one system with the dual goal of estimating that system and predicting for other potentially different systems. Transferability, also known as generalisability or in some setups extrapolation, is a common ambition in many disciplines. This motivates to a discussion of the duality between loss functions and priors in Bayesian modelling and analysis.
Kerrie Mengersen is a Distinguished Professor of Statistics at the Queensland University of Technology. She is an Australian Research Council Laureate Fellow and a Deputy Director of the Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers. In addition to being a Fellow of the Australian Academy of Science, Professor Mengersen is a Past President of the Statistical Society of Australia and the International Society for Bayesian Analysis, and an elected Fellow of the Institute for Mathematical Sciences, the Royal Statistical Society and the International Statistical Institute, among others. Professor Mengersen works on a range of methodological and applied problems in statistics. These include Bayesian methods, causation and association, big data and complex systems, with translation to the fields of environment, health, industry and society. More details on her outstanding career can be found at her webpage: http://staff.qut.edu.au/staff/mengerse/
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