Simulation-based inference (SBI) methods such as approximate Bayesian computation
(ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating …

Modern approaches for simulation-based inference build upon deep learning surrogates to
enable approximate Bayesian inference with computer simulators. In practice, the estimated …

This paper proposes an online, provably robust, and scalable Bayesian approach for
changepoint detection. The resulting algorithm has key advantages over previous work: it …

Aided by advances in neural density estimation, considerable progress has been made in
recent years towards a suite of simulation-based inference (SBI) methods capable of …

We present extensive empirical evidence showing that current Bayesian simulation-based
inference algorithms can produce computationally unfaithful posterior approximations. Our …

Recent advances in probabilistic deep learning enable efficient amortized Bayesian
inference in settings where the likelihood function is only implicitly defined by a simulation …

Likelihood-free inference methods typically make use of a distance between simulated and
real data. A common example is the maximum mean discrepancy (MMD), which has …

Bayesian inference usually requires running potentially costly inference procedures
separately for every new observation. In contrast, the idea of amortized Bayesian inference …

Discrete state spaces represent a major computational challenge to statistical inference,
since the computation of normalization constants requires summation over large or possibly …

Bayesian inference is a powerful framework for making probabilistic inferences and
decisions under uncertainty. Fundamental choices in modern Bayesian workflows concern …