Orateur
Will Handley
(University Of Cambridge)
Description
Nested Sampling is an established numerical technique for optimising, sampling, integrating and scanning a priori unknown probability distributions. Whilst typically used in the context of traditional likelihood-driven Bayesian inference, it's capacity as a general sampler means that it is capable of exploring distributions on data [2105.13923] and joint spaces [1606.03757].
In this talk I will give a brief outline of the points of difference of nested sampling in comparison with other techniques, what it can uniquely offer in tackling the challenge of likelihood-free inference, and discuss ongoing work with collaborators in applying it in a variety of LFI-based approaches.
Auteur principal
Will Handley
(University Of Cambridge)
