1k citations · 4.9k across the 40 of their papers we have counts for
40 papers
From Evidence to Evident: Decisive Cosmological Evidence for the Normal Neutrino Mass Hierarchy
Raul Jimenez, Carlos Peña Garay, Fergus Simpson +1
Cosmological data have reached the precision needed to turn the neutrino mass ordering from a weak Bayesian preference into a decisive model-selection test. We compute the evidence…
The Coherence Principle: A Falsifiable Prior for Model Selection from the Grammar of Theories
Raul Jimenez, Carlos Peña Garay, Fergus Simpson +1
Bayesian model selection in cosmology and particle physics is often performed where posterior odds inherit a strong, often unacknowledged dependence on the prior assigned to compet…
Neural Diffusion Processes
Vincent Dutordoir, Alan Saul, Zoubin Ghahramani +1
Neural network approaches for meta-learning distributions over functions have desirable properties such as increased flexibility and a reduced complexity of inference. Building on…
Neutrino Masses and Mass Hierarchy: Evidence for the Normal Hierarchy
Raul Jimenez, Carlos Pena-Garay, Kathleen Short +2
The latest cosmological constraints on the sum of neutrino masses, in combination with the latest laboratory measurements on oscillations, provide ``decisive" Bayesian evidence for…
Validating Gaussian Process Models with Simulation-Based Calibration
John Mcleod, Fergus Simpson
Gaussian process priors are a popular choice for Bayesian analysis of regression problems. However, the implementation of these models can be complex, and ensuring that the impleme…
Kernel Identification Through Transformers
Fergus Simpson, Ian Davies, Vidhi Lalchand +3
Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models, as the chosen kernel determines both the inductive biases and prior support of…