Nested sampling: powering next-generation inference and machine learning tools for astrophysics, cosmology, particle physics and beyond
Speaker: Will Handley
Affiliation: Cambridge University
Abstract
Nested sampling is a radical alternative to traditional MCMC techniques for integrating and exploring probability distributions. With publicly available implementations such as MultiNest, PolyChord, dynesty and ultranest, it has become widely adopted across science as a powerful tool for parameter estimation, model comparison and tension quantification.
In this talk I will give a pedagogical introduction to the theory & practice of nested sampling, and illustrate with recent applications in astrophysics, cosmology, particle physics, machine learning and beyond. I will finish with a discussion of recent innovations in the nested sampling toolkit, and prospects for the frontier of the field.
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Venue
Room: 222
