Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a
stochastic simulator and inferring posterior distributions from model-simulations. To improve …

While astrophysical and cosmological probes provide a remarkably precise and consistent
picture of the quantity and general properties of dark matter, its fundamental nature remains …

We analyze the ringdown phase of the first detected black-hole merger, GW150914, using a
simulation-based inference pipeline based on masked autoregressive flows. We obtain …

Likelihood-to-evidence ratio estimation is usually cast as either a binary (NRE-A) or a
multiclass (NRE-B) classification task. In contrast to the binary classification framework, the …

Sampling-based inference techniques are central to modern cosmological data analysis;
these methods, however, scale poorly with dimensionality and typically require approximate …

Parametric stochastic simulators are ubiquitous in science, often featuring high-dimensional
input parameters and/or an intractable likelihood. Performing Bayesian parameter inference …