12 papers
Ab Initio Real-Time Gravitational-Wave Parameter Estimation
David Yallup, Metha Prathaban, James Alvey +4
The paper introduces a GPU‑optimized nested sampling algorithm that can perform rapid, full‑waveform gravitational‑wave parameter estimation for binary neutron star events, achievi…
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…
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 $Î…
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 oth…
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…
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…