158 citations · 208 across the 18 of their papers we have counts for
20 papers
Neural Transport Nested Sampling
David Yallup, Will Handley
Sampling from Boltzmann distributions of molecular systems is an inference problem that has seen significant recent developments fuelled by advances in neural density estimation. W…
Quenched Ensemble Sampling
David Yallup
Some of the sharpest challenges in sampling from the energy functions of physical systems arise at phase transitions, where the density of states changes abruptly and many sampling…
Ab Initio Real-Time Gravitational-Wave Parameter Estimation
David Yallup, Metha Prathaban, James Alvey +4
We present a specialised GPU-native nested sampling kernel targeting rapid parameter estimation for gravitational wave inference problems. Building upon a Slice-within-Gibbs (SwiG)…
Rapid Hubble constant inference from GW170817 using GPU-accelerated nested sampling: prior sensitivity and the limits of post-hoc reweighting
Ming Han Yang, Metha Prathaban, David Yallup +1
The bright-siren measurement of the Hubble constant from GW170817 (Abbott et al. 2017) assumes that switching from a volumetric to a uniform-in- luminosity-distance prior can…
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…
The Bayesian view of DESI DR2 with unimpeded: Evidence and tension in a combined analysis with CMB and supernovae across cosmological models
Dily Duan Yi Ong, David Yallup, Will Handley
We apply the framework to perform a fully Bayesian reanalysis of the DESI DR2 data, using nested sampling with to compute evidences for $Λ…
Time delays and stationarity in quasar light curves
Namu Kroupa, David Yallup, Will Handley
We present a fully Bayesian framework for time delay inference and stationarity tests in quasar light curves using marginalised Gaussian processes. The model separates a determinis…
Nested Sampling with Slice-within-Gibbs: Efficient Evidence Calculation for Hierarchical Bayesian Models
David Yallup
We present Nested Sampling with Slice-within-Gibbs (NS-SwiG), an algorithm for Bayesian inference and evidence estimation in high-dimensional models whose likelihood admits a facto…
Nested Slice Sampling: Vectorized Nested Sampling for GPU-Accelerated Inference
David Yallup, Namu Kroupa, Will Handley
Model comparison and calibrated uncertainty quantification often require integrating over parameters, but scalable inference can be challenging for complex, multimodal targets. Nes…
A Bayesian Perspective on Evidence for Evolving Dark Energy
Dily Duan Yi Ong, David Yallup, Will Handley
The DESI Collaboration reports a significant preference for a dynamic dark energy model (CDM) over the cosmological constant (CDM) when their data are combined with othe…
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…
Parallel Nested Slice Sampling for Gravitational Wave Parameter Estimation
David Yallup, Metha Prathaban, James Alvey +1
Inferring parameters and testing hypotheses from gravitational wave signals is a computationally intensive task central to modern astrophysics. Nested sampling, a Bayesian inferenc…
Gravitational-wave inference at GPU speed: A bilby-like nested sampling kernel within blackjax-ns
Metha Prathaban, David Yallup, James Alvey +3
We present a GPU-accelerated implementation of the gravitational-wave Bayesian inference pipeline for parameter estimation and model comparison. Specifically, we implement the `acc…
Dynamic or Systematic? Bayesian model selection between dark energy and supernova biases
A. N. Ormondroyd, W. J. Handley, M. P. Hobson +2
The Dark Energy Survey 5-year (DES-5Y) supernovae, combined with Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillations, appear to favour Chevallier-Polarski-Lind…
Kernel-, mean- and noise-marginalised Gaussian processes for exoplanet transits and inference
Namu Kroupa, David Yallup, Will Handley +1
Using a fully Bayesian approach, Gaussian Process regression is extended to include marginalisation over the kernel choice and kernel hyperparameters. In addition, Bayesian model c…
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…