One of the challenges of high-dimensional data is identifying the true information contained therein. In this presentation, I will describe some approaches that we have been considering to address this challenge. These approaches include Bayesian methods of matrix factorisation, intrinsic dimension and structured variable selection. Our work on these methods is set in the context of substantive case studies in image analysis, sport and genomics.
Kerrie Mengersen is a Distinguished Professor of Statistics at the Queensland University of Technology in Brisbane, Australia. She is a Deputy Director of the Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers, an ARC Laureate Fellow, and the Director of the QUT Centre for Data Science. Kerrie’s research interests focus on the development of (mostly Bayesian) models and computational algorithms, and their application to substantive challenges in health, the environment, and industry.