The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

Markov chain Monte Carlo (MCMC) is one of the most useful approaches to scientific
computing because of its flexible construction, ease of use, and generality. Indeed, MCMC is …

There is increasing availability and use of unstructured and semi‐structured citizen science
data in biodiversity research and conservation. This expansion of a rich source of 'big …

Federated learning aims to learn a global model that performs well on client devices with
limited cross-client communication. Personalized federated learning (PFL) further extends …

Statistical models of text have become increasingly popular in statistics and computer
science as a method of exploring large document collections. Social scientists often want to …

In this paper, we study a method to sample from a target distribution π over R^d having a
positive density with respect to the Lebesgue measure, known up to a normalisation factor …

The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …

Occupancy modelling is a common approach to assess species distribution patterns, while
explicitly accounting for false absences in detection–nondetection data. Numerous …

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of
underlying causes. However, determining which model parameters agree with complex and …

High-dimensional Bayesian inference via the unadjusted Langevin algorithm Page 1
Bernoulli 25(4A), 2019, 2854–2882 https://doi.org/10.3150/18-BEJ1073 High-dimensional …