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.

About Physics colloquium

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Venue

Parnell Building (07)
Room: 222