158 citations · 208 across the 18 of their papers we have counts for
5 papers · 1 filter
Hunting for bumps in the margins
David Yallup, Will Handley
Data driven modelling is vital to many analyses at collider experiments, however the derived inference of physical properties becomes subject to details of the model fitting proced…
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
Pablo Lemos, Miles Cranmer, Muntazir Abidi +5
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys. Despite continual improvements…
Nested sampling for physical scientists
Greg Ashton, Noam Bernstein, Johannes Buchner +20
We review Skilling's nested sampling (NS) algorithm for Bayesian inference and more broadly multi-dimensional integration. After recapitulating the principles of NS, we survey deve…
Split personalities in Bayesian Neural Networks: the case for full marginalisation
David Yallup, Will Handley, Mike Hobson +2
The true posterior distribution of a Bayesian neural network is massively multimodal. Whilst most of these modes are functionally equivalent, we demonstrate that there remains a le…
Exploring phase space with Nested Sampling
David Yallup, Timo Janßen, Steffen Schumann +1
We present the first application of a Nested Sampling algorithm to explore the high-dimensional phase space of particle collision events. We describe the adaptation of the algorith…