Adam Coogan

Nikolay Malkin

2025-11-23

ArXiv (preprint)

caskade: building Pythonic scientific simulators

Connor Stone

Alexandre Adam

2025-09-14

Journal of Open Source Software (published)

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Connor Stone

Alexandre Adam

M. J. Yantovski-Barth

Andreas Filipp

Landung Setiawan

Cordero Core

Ronan Legin

Charles Wilson

Gabriel Missael Barco

2023-12-31

J. Open Source Softw. (published)

Time Delay Cosmography with a Neural Ratio Estimator

Ève Campeau-Poirier

We explore the use of a Neural Ratio Estimator (NRE) to determine the Hubble constant (…

2023-09-26

ArXiv (preprint)

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos

Parameter inference, i.e. inferring the posterior distribution of the parameters of a statistical model given some data, is a central proble… (see more)m to many scientific disciplines. Generative models can be used as an alternative to Markov Chain Monte Carlo methods for conducting posterior inference, both in likelihood-based and simulation-based problems. However, assessing the accuracy of posteriors encoded in generative models is not straightforward. In this paper, we introduce `Tests of Accuracy with Random Points' (TARP) coverage testing as a method to estimate coverage probabilities of generative posterior estimators. Our method differs from previously-existing coverage-based methods, which require posterior evaluations. We prove that our approach is necessary and sufficient to show that a posterior estimator is accurate. We demonstrate the method on a variety of synthetic examples, and show that TARP can be used to test the results of posterior inference analyses in high-dimensional spaces. We also show that our method can detect inaccurate inferences in cases where existing methods fail.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (published)

proceedings.mlr.press

Posterior samples of source galaxies in strong gravitational lenses with score-based priors

Yoshua Bengio

Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge,… (see more) in part due to the difficulties of accurately quantifying the priors. Here, we report the use of a score-based model to encode the prior for the inference of undistorted images of background galaxies. This model is trained on a set of high-resolution images of undistorted galaxies. By adding the likelihood score to the prior score and using a reverse-time stochastic differential equation solver, we obtain samples from the posterior. Our method produces independent posterior samples and models the data almost down to the noise level. We show how the balance between the likelihood and the prior meet our expectations in an experiment with out-of-distribution data.

2022-11-06

ArXiv (preprint)