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

By Justin Alsing, Tom Charnock, S. Feeney, Benjamin Dan Wandelt. 2019

214 citations recorded by the source corpus.

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 datasets, 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 $\mathcal {O}(10^3)$ 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 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.

Subfields: Artificial Intelligence, Astronomy and Astrophysics, Statistics and Probability, Probabilistic & Bayesian Methods, AI for Physical Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gilles Louppe
2. J. Brehmer
3. Kyle Cranmer
4. Joeri Hermans
5. Benjamin Dan Wandelt
6. Justin Alsing
7. Siddharth Mishra-Sharma
8. Yu Feng

In Astronomy and Astrophysics

1. Benjamin Dan Wandelt
2. L. Verde
3. M. Zaldarriaga
4. Shirley Ho
5. David N. Spergel
6. E. Castorina
7. Yu Feng
8. Justin Alsing

In Statistics and Probability

1. J. Brehmer
2. Kyle Cranmer
3. Gilles Louppe
4. Benjamin Dan Wandelt
5. Justin Alsing
6. Joeri Hermans
7. Stéphane Mallat
8. Tom Charnock

In Probabilistic & Bayesian Methods

1. J. Brehmer
2. Gilles Louppe
3. Kyle Cranmer
4. Joeri Hermans
5. Benjamin Dan Wandelt
6. Justin Alsing
7. Siddharth Mishra-Sharma
8. M. Viel

In AI for Physical Sciences

1. Gilles Louppe
2. Kyle Cranmer
3. J. Brehmer
4. Benjamin Dan Wandelt
5. Justin Alsing
6. Joeri Hermans
7. Tom Charnock
8. Siddharth Mishra-Sharma

Citations in the corpus

• Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning (2019, background): “…et al. 2017; Louppe & Cranmer 2017; Lueckmann et al. 2017; Papamakarios et al. 2017; Tran et al. 2017; Chen et al. 2018; Dinev & Gutmann 2018; Grathwohl et al. 2018; Huang et al. 2018; Kingma & Dhariwal 2018; Lueckmann et al. 2018; Papamakarios et al. 2018; Alsing et al. 2019; Hermans et al. 2019).”
• The Quijote Simulations (2019, background): “Charnock et al. 2018; Alsing et al. 2019).”
• Nuisance hardened data compression for fast likelihood-free inference (2019, methodology): “Mixture density networks (MDN) and masked autoregressive flows (MAF) have proven useful conditional density estimators in this context (Papamakarios & Murray 2016; Papamakarios et al. 2018; Alsing et al. 2019).”