Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters. - Conor Rosato; Paul R. Horridge; Thomas B. Schön; Simon Maskell (CoRR, 2021)

Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal. - Conor Rosato; John Harris; Jasmina Panovska-Griffiths; Simon Maskell (FUSION, 2022)

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)

An O(log2 N) SMC2 Algorithm on Distributed Memory with an Approx. Optimal L-Kernel. - Conor Rosato; Alessandro Varsi; Joshua Murphy; Simon Maskell (SDF-MFI, 2023)

Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models. - Conor Rosato; Robert E. Moore; Matthew Carter; John Heap; John Harris; Jose Storopoli; Simon Maskell (Inf., 2023)

Parameterizing Hierarchical Particle Filters with Concept Drift for Time-varying Parameter Estimation. - Joshua Murphy; Conor Rosato; Andrew Millard; Simon Maskell (APSIPA, 2024)

Enhanced SMC2: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals. - Conor Rosato; Joshua Murphy; Alessandro Varsi; Paul R. Horridge; Simon Maskell (MFI, 2024)

Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review. - Conor Rosato; Peter L. Green; John Harris; Simon Maskell; William Hope; Alessandro Gerada; Alex Howard (IEEE Access, 2024)

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)