Predicting events in the real world with a computer model (simulator) is challenging. Every
simulator, to varying extents, has model discrepancy, a mismatch between real world …

Monitoring and fault detection methods are increasingly important to achieve a robust and
resource efficient operation of wastewater treatment plants (WWTPs). The purpose of this …

The Gaussian process as a tool for, predominantly, regression tasks in machine learning
has only been growing in popularity over recent years. Although not reaching the same …

Gaussian processes (GPs) for machine learning have been studied systematically over the
past two decades. However, kernel design for GPs and the associated hyper-parameters …

Time series analysis aims to understand underlying patterns and relationships in data to
inform decision-making. As time series data are becoming more widely available across a …

By facilitating the generation of samples from arbitrary probability distributions, Markov
Chain Monte Carlo (MCMC) is, arguably, the tool for the evaluation of Bayesian inference …

Within the offshore industry Structural Health Monitoring remains a growing area of interest.
The oil and gas sectors are faced with ageing infrastructure and are driven by the desire for …

Many machine learning problems can be framed in the context of estimating functions, and
often these are time-dependent functions that are estimated in real-time as observations …

Several disciplines, such as econometrics, neuroscience, and computational psychology,
study the dynamic interactions between variables over time. A Bayesian nonparametric …

Time-dependent data often exhibit characteristics, such as non-stationarity and heavy-tailed
errors, that would be inappropriate to model with the typical assumptions used in popular …