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 …

In recent years, the shortcomings of Bayes posteriors as inferential devices has received
increased attention. A popular strategy for fixing them has been to instead target a Gibbs …

Whether and how data scientists, statisticians and modellers should be accountable for the
AI systems they develop remains a controversial and highly debated topic, especially given …

Motivated by big data and the vast parameter spaces in modern machine learning models,
optimisation approaches to Bayesian inference have seen a surge in popularity in recent …

Robust Bayesian inference using density power divergence (DPD) has emerged as a
promising approach for handling outliers in statistical estimation. While the DPD-based …