Neural posterior estimation methods based on discrete normalizing flows have become
established tools for simulation-based inference (SBI), but scaling them to high-dimensional …

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 …

Neural posterior estimation methods based on discrete normalizing flows have become
established tools for simulation-based inference (SBI), but scaling them to high-dimensional …

A bstract The likelihood ratio is a crucial quantity for statistical inference in science that
enables hypothesis testing, construction of confidence intervals, reweighting of distributions …

Conservative inference is a major concern in simulation-based inference. It has been shown
that commonly used algorithms can produce overconfident posterior approximations …

We propose E-C2ST, a classifier two-sample test for high-dimensional data based on E-
values. Compared to $ p $-values-based tests, tests with E-values have finite sample …

Cosmological inferences typically rely on explicit expressions for the likelihood and
covariance of the data vector, which normally consists of a set of summary statistics …

We present Surjective Sequential Neural Likelihood (SSNL) estimation, a novel method for
simulation-based inference in models where the evaluation of the likelihood function is not …

Abstract Simulation-Based Inference (SBI) is a common name for an emerging family of
approaches that infer the model parameters when the likelihood is intractable. Existing SBI …

Amortized simulation-based inference (SBI) methods train neural networks on simulated
data to perform Bayesian inference. While this approach avoids the need for tractable …