Mainstream statistical methodology is generally applicable to data observed in Euclidean
space. There are, however, numerous contexts of considerable scientific interest in which …

The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

Generalised Bayesian inference updates prior beliefs using a loss function, rather than a
likelihood, and can therefore be used to confer robustness against possible mis …

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

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 …

Bayesian model comparison (BMC) offers a principled probabilistic approach to study and
rank competing models. In standard BMC, we construct a discrete probability distribution …

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

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 …

We propose a new method for conducting Bayesian prediction that delivers accurate
predictions without correctly specifying the unknown true data generating process. A prior is …