Bayesian experimental design is a fast growing area of research with many real‐world
applications. As computational power has increased over the years, so has the development of …

Compared with some other species, insulin dysregulation in equids is poorly understood.
However, hyperinsulinemia causes laminitis, a significant and often lethal disease affecting the …

Leaf traits are frequently measured in ecology to provide a ‘common currency’ for predicting
how anthropogenic pressures impact ecosystem function. Here, we test whether leaf traits …

This article presents a sequential Monte Carlo (SMC) algorithm that can be used for any one-at-a-time
Bayesian sequential design problem in the presence of model uncertainty where …

Monitoring the water quality of rivers is increasingly conducted using automated in situ
sensors, enabling timelier identification of unexpected values or trends. However, the data are …

Big Datasets are endemic, but are often notoriously difficult to analyse because of their size,
heterogeneity and quality. The purpose of this paper is to open a discourse on the potential …

In this paper we present a sequential Monte Carlo algorithm for Bayesian sequential
experimental design applied to generalised non-linear models for discrete data. The approach is …

In this article we briefly examine the unique features of Single-Case Designs (SCDs) (studies
in a single participant), their history and current trends, and real-world clinical applications. …

Background: N-of-1 trials offer an innovative approach to delivering personalized clinical
care together with population-level research. While increasingly used, these methods have …

Background Endocrinopathic laminitis is common in horses and ponies, but the recurrence
rate of the disease is poorly defined. Objectives To determine the incidence of, and risk …