most citedFast likelihood-free cosmology with neural density estimators and active learning

219 citations · 337 across the 2 of their papers we have counts for

collaborators

5 papers

astro-ph.IM2019

SPECULATOR: Emulating stellar population synthesis for fast and accurate galaxy spectra and photometry

Justin Alsing, Hiranya Peiris, Joel Leja +6

We present SPECULATOR - a fast, accurate, and flexible framework for emulating stellar population synthesis (SPS) models for predicting galaxy spectra and photometry. For emulating…

astro-ph.CO2019

Cosmic Shear: Inference from Forward Models

Peter L. Taylor, Thomas D. Kitching, Justin Alsing +3

Density-estimation likelihood-free inference (DELFI) has recently been proposed as an efficient method for simulation-based cosmological parameter inference. Compared to the standa…

astro-ph.CO2019118 cited

Nuisance hardened data compression for fast likelihood-free inference

Justin Alsing, Benjamin Wandelt

In this paper we show how nuisance parameter marginalized posteriors can be inferred directly from simulations in a likelihood-free setting, without having to jointly infer the hig…

astro-ph.CO2019219 cited

Fast likelihood-free cosmology with neural density estimators and active learning

Justin Alsing, Tom Charnock, Stephen Feeney +1

Likelihood-free inference provides a framework for performing rigorous Bayesian inference using only forward simulations, properly accounting for all physical and observational eff…

astro-ph.CO2016

Bayesian hierarchical modelling of weak lensing - the golden goal

Alan Heavens, Justin Alsing, Andrew Jaffe +3

To accomplish correct Bayesian inference from weak lensing shear data requires a complete statistical description of the data. The natural framework to do this is a Bayesian Hierar…