6 papers · 1 filter
Improved Disease Outbreak Detection from Out-of-sequence measurements Using Markov-switching Fixed-lag Particle Filters
Conor Rosato, Joshua Murphy, Siân E. Jenkins +7
Particle filters (PFs) have become an essential tool for disease surveillance, as they can estimate hidden epidemic states in nonlinear and non-Gaussian models. In epidemic modelli…
Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals
Joshua Murphy, Conor Rosato, Andrew Millard +3
When performing Bayesian inference using Sequential Monte Carlo (SMC) methods, two considerations arise: the accuracy of the posterior approximation and computational efficiency. T…
Assessing the Impact of Vaccination on Rotavirus Transmission Dynamics Using Bayesian Inference
Conor Rosato, Joshua Murphy, Simon Maskell +1
The introduction of the rotavirus vaccine in the United Kingdom (UK) in 2013 led to a noticeable decline in laboratory reports in subsequent years. To assess the impact of vaccinat…
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles
Andrew Millard, Zheng Zhao, Joshua Murphy +1
Sequential Monte Carlo (SMC) methods offer a principled approach to Bayesian uncertainty quantification but are traditionally limited by the need for full-batch gradient evaluation…
Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks
Andrew Millard, Joshua Murphy, Simon Maskell +1
Partial Bayesian neural networks (pBNNs) have been shown to perform competitively with fully Bayesian neural networks while only having a subset of the parameters be stochastic. Us…
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers
Andrew Millard, Joshua Murphy, Daniel Frisch +1
Markov chain Monte Carlo (MCMC) methods are a powerful but computationally expensive way of performing non-parametric Bayesian inference. MCMC proposals which utilise gradients, su…