Alsing, J;
Charnock, T;
Feeney, S;
Wandelt, B;
(2019)
Fast likelihood-free cosmology with neural density estimators and active learning.
Monthly Notices of the Royal Astronomical Society
, 488
(3)
pp. 4440-4458.
10.1093/mnras/stz1960.
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Abstract
Likelihood-free inference provides a framework for performing rigorous Bayesian inference using only forward simulations, properly accounting for all physical and observational effects that can be successfully included in the simulations. The key challenge for likelihood-free applications in cosmology, where simulation is typically expensive, is developing methods that can achieve high-fidelity posterior inference with as few simulations as possible. Density-estimation likelihood-free inference (DELFI) methods turn inference into a density-estimation task on a set of simulated data-parameter pairs, and give orders of magnitude improvements over traditional Approximate Bayesian Computation approaches to likelihood-free inference. In this paper, we use neural density estimators (NDEs) to learn the likelihood function from a set of simulated data sets, with active learning to adaptively acquire simulations in the most relevant regions of parameter space on the fly. We demonstrate the approach on a number of cosmological case studies, showing that for typical problems high-fidelity posterior inference can be achieved with just O(103) simulations or fewer. In addition to enabling efficient simulation-based inference, for simple problems where the form of the likelihood is known, DELFI offers a fast alternative to Markov Chain Monte Carlo (MCMC) sampling, giving orders of magnitude speed-up in some cases. Finally, we introduce PYDELFI – a flexible public implementation of DELFI with NDEs and active learning – available at https://github.com/justinalsing/pydelfi.
| Type: | Article |
|---|---|
| Title: | Fast likelihood-free cosmology with neural density estimators and active learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/stz1960 |
| Publisher version: | https://doi.org/10.1093/mnras/stz1960 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Physical Sciences, Astronomy & Astrophysics, data analysis: methods, APPROXIMATE BAYESIAN COMPUTATION, DATA-COMPRESSION, POWER SPECTRUM, FREE INFERENCE, WEAK, MAGNIFICATION, PARAMETERS, BARYONS |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10087563 |
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