A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

A prompt public health response to a new epidemic relies on the ability to monitor and
predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK …

Marginal likelihood has been proposed as a genetic programming-based symbolic
regression (GPSR) fitness metric to prevent overly complex expressions and overfitting …

The aim of the history matching method is to locate nonimplausible regions of the parameter
space of complex deterministic or stochastic models by matching model outputs with data. It …

A prompt public health response to a new epidemic relies on the ability to monitor and
predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK …

The aim of the history matching method is to locate non-implausible regions of the
parameter space of complex deterministic or stochastic models by matching model outputs …

Stochastic Gradient Langevin Dynamics (SGLD) have been widely used for Bayesian
sampling from certain probability distributions, incorporating derivatives of the log-posterior …

Vector symbolic algebras (VSAs) are modelling frameworks that unify human cognition and
neural network models, and some have recently been shown to be probabilistic models akin …

Santiago Ram ny Cajal first traced the microscopic intricacies of individual neurons in the
late 19th century. His work revolutionised neuroscience, and earned him the moniker “the …