3 papers
cs.LG2026
Automatic Laplace Collapsed Sampling: Scalable Marginalisation of Latent Parameters via Automatic Differentiation
Toby Lovick, David Yallup, Will Handley
We present Automatic Laplace Collapsed Sampling (ALCS), a general framework for marginalising latent parameters in Bayesian models using automatic differentiation, which we combine…
astro-ph.CO2025
High-Dimensional Bayesian Model Comparison in Cosmology with GPU-accelerated Nested Sampling and Neural Emulators
Toby Lovick, David Yallup, Davide Piras +2
We demonstrate a GPU-accelerated nested sampling framework for efficient high-dimensional Bayesian inference in cosmology. Using JAX-based neural emulators and likelihoods for cosm…
astro-ph.CO2023
Non-Gaussian Likelihoods for Type Ia Supernovae Cosmology: Implications for Dark Energy and
Toby Lovick, Suhail Dhawan, Will Handley
The latest improvements in the scale and calibration of Type Ia supernovae catalogues allow us to constrain the specific nature and evolution of dark energy through its effect on t…