Bayesian model selection for likelihood-based and simulation-based inference
Date: Tuesday, 11 October 2022
Time: 16:00 (UTC)
Speaker: Prof. Jason McEwen (University College London, UK)
Contact the organizer: Andriy Olenko a.olenko@latrobe.edu.au
Abstract:
In the study of cosmology, where we seek to uncover an understanding of the fundamental physical processes underlying the origin, content, and evolution of our Universe, we are not blessed with the ability to perform experiments -- rather, we have only one Universe to observe. In this scenario, while we are of course interested in estimating the parameters of models describing the physical processes observed, we are often most interested in selecting the best underlying model, which has given rise to the prevalence of Bayesian model selection in cosmology and astrophysics. While I will motivate recent developments in Bayesian model selection from problems in cosmology and astrophysics, I will mostly focus on new methodological advances. I will discuss new approaches that leverage ideas across statistics, optimization and machine learning to bring to bear the respective strengths of these paradigms to the highly computationally challenging problem of Bayesian model selection. In particular, I will review the learnt harmonic mean estimator for both likelihood-based and simulation-based inference and the proximal nested sampling framework for high-dimensional model selection.
Meeting ID: 869 0750 1750
Passcode: 621898
