The Kalman filter—or, more precisely, the extended Kalman filter (EKF)—is a fundamental
engineering tool that is pervasively used in control and robotics and for various estimation …

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 …

Characterizing statistical properties of solutions of inverse problems is essential for decision
making. Bayesian inversion offers a tractable framework for this purpose, but current …

An important task in machine learning and statistics is the approximation of a probability
measure by an empirical measure supported on a discrete point set. Stein Points are a class …

In this chapter, we identify fundamental geometric structures that underlie the problems of
sampling, optimization, inference, and adaptive decision-making. Based on this …

The scandals in publicly listed companies have highlighted the large losses that can result
from financial statement fraud and weak corporate governance. Machine learning …

As one of the most significant part of carbon neutralisation, the rapid growth of electric
vehicle (EV) market in past few years has greatly expedited the transport electrification …

A complete recipe of measure-preserving diffusions in Euclidean space was recently
derived unifying several MCMC algorithms into a single framework. In this paper, we …

In this paper, we propose irreversible versions of the Metropolis–Hastings (MH) and
Metropolis-adjusted Langevin algorithm (MALA) with a main focus on the latter. For the …

Stochastically evolving geometric systems are studied in shape analysis and computational
anatomy for modeling random evolutions of human organ shapes. The notion of geodesic …