M. A. Price

Latest

• Generative modelling for mass-mapping with fast uncertainty quantification
• Generative models of astrophysical fields with scattering transforms on the sphere
• Scattering transforms on the sphere: application to large scale structure modelling
• Using conditional GANs for convergence map reconstruction with uncertainties
• The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
• Learned harmonic mean estimation of the Bayesian evidence with normalizing flows
• Differentiable and accelerated spherical harmonic and Wigner transforms
• Differentiable and accelerated wavelet transforms on the sphere and ball
• Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging
• Bayesian model comparison for simulation-based inference
• Fast emulation of anisotropies induced in the cosmic microwave background by cosmic strings
• Learned harmonic mean estimation of the marginal likelihood with normalizing flows
• Proximal nested sampling with data-driven priors for physical scientists
• Scalable and equivariant spherical CNNs by discrete-continuous (DISCO) convolutions
• Machine learning assisted Bayesian model comparison: learnt harmonic mean estimator
• Mapping dark matter on the celestial sphere with weak gravitational lensing
• Bayesian variational regularization on the ball
• Sparse image reconstruction on the sphere: a general approach with uncertainty quantification
• Efficient generalized spherical CNNs
• Sparse Bayesian mass-mapping with uncertainties: full-sky observations on the celestial sphere
• Sparse Bayesian mass-mapping with uncertainties: hypothesis testing of structure
• Scale-discretised ridgelet transform on the sphere
• Sparse Bayesian mass-mapping with uncertainties
• Sparse Bayesian mass-mapping with uncertainties: local credible intervals
• Sparse Bayesian mass-mapping with uncertainties: peak statistics and feature locations