Stein's method compares probability distributions through the study of a class of linear
operators called Stein operators. While mainly studied in probability and used to underpin …

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 …

Simulator-based models are models for which the likelihood is intractable but simulation of
synthetic data is possible. They are often used to describe complex real-world phenomena …

Differential privacy guarantees allow the results of a statistical analysis involving sensitive
data to be released without compromising the privacy of any individual taking part …

The use of heuristics to assess the convergence and compress the output of Markov chain
Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced …

We establish the first mathematically rigorous link between Bayesian, variational Bayesian,
and ensemble methods. A key step towards this it to reformulate the non-convex …

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

Statisticians often face the choice between using probability models or a paradigm defined
by minimising a loss function. Both approaches are useful and, if the loss can be re-cast into …

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