Speaker
Metha Prathaban
(University of Cambridge)
Description
Nested sampling is a tool for posterior estimation and model comparison across a wide variety of cross-disciplinary fields, and is used in Simulation Based Inference and AI emulation. This talk explores the performance and accuracy gains to be made in high dimensional nested sampling by rescuing the discarded likelihood evaluations available in present nested sampling runs, and is thus useful to all users of nested sampling.
Primary author
Metha Prathaban
(University of Cambridge)
Co-author
Dr
William Handley
(University of Cambridge)
