10 papers
Reliability of Probabilistic Emulation of Physical Systems
Sam F. Greenbury, Radka Jersakova, Paolo Conti +4
Two dominant approaches have emerged for generating probabilistic forecasts of physical systems: generative models, such as diffusion or flow matching; and ensembles of determinist…
Learned harmonic mean estimation of the marginal likelihood for multimodal posteriors with flow matching
Alicja Polanska, Jason D. McEwen
The marginal likelihood, or Bayesian evidence, is a crucial quantity for Bayesian model comparison but its computation can be challenging for complex models, even in parameters spa…
Learned harmonic mean estimation of the Bayesian evidence with normalizing flows
Alicja Polanska, Matthew A. Price, Davide Piras +2
We present the learned harmonic mean estimator with normalizing flows - a robust, scalable and flexible estimator of the Bayesian evidence for model comparison. Since the estimator…
Generative modelling for mass-mapping with fast uncertainty quantification
Jessica J. Whitney, TobÃas I. Liaudat, Matthew A. Price +2
Understanding the nature of dark matter in the Universe is an important goal of modern cosmology. A key method for probing this distribution is via weak gravitational lensing mass-…
Generative imaging for radio interferometry with fast uncertainty quantification
Matthijs Mars, TobÃas I. Liaudat, Jessica J. Whitney +2
With the rise of large radio interferometric telescopes, particularly the SKA, there is a growing demand for computationally efficient image reconstruction techniques. Existing rec…
Differentiable and accelerated spherical harmonic and Wigner transforms
Matthew A. Price, Jason D. McEwen
Many areas of science and engineering encounter data defined on spherical manifolds. Modelling and analysis of spherical data often necessitates spherical harmonic transforms, at h…