Efficient Learning of the Parameters of Non-Linear Models Using Differentiable Resampling in Particle Filters. - Conor Rosato; Lee Devlin; Vincent Béraud; Paul R. Horridge; Thomas B. Schön; Simon Maskell (IEEE Trans. Signal Process., 2022)

The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler Without Accept/Reject. - Lee Devlin; Matthew Carter; Paul R. Horridge; Peter L. Green; Simon Maskell (IEEE Signal Process. Lett., 2024)

A General-Purpose Fixed-Lag No U-Turn Sampler for Nonlinear Non-Gaussian State Space Models. - Alessandro Varsi; Lee Devlin; Paul R. Horridge; Simon Maskell (IEEE Trans. Aerosp. Electron. Syst., 2024)

An Entropic Metric for Measuring Calibration of Machine Learning Models. - Daniel James Sumler; Lee Devlin; Simon Maskell; Richard O. Lane (CoRR, 2025)

Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods. - Conor Rosato; Harvinder Lehal; Simon Maskell; Lee Devlin; Malcolm Strens (CoRR, 2025)

Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals. - Joshua Murphy; Conor Rosato; Andrew Millard; Lee Devlin; Paul R. Horridge; Simon Maskell (CoRR, 2025)