10 papers
Sampling from Neural Operator Priors to Solve Partial Differential Equations
Andrew Millard, Joshua Murphy, Zheng Zhao
Guided sampling with sparse observations and PDE residuals has recently been combined with neural operator priors to solve partial differential equations in function space, their n…
Investigating Batch Inference in a Sequential Monte Carlo Framework for Neural Networks
Andrew Millard, Joshua Murphy, Peter Green +1
Bayesian inference allows us to define a posterior distribution over the weights of a generic neural network (NN). Exact posteriors are usually intractable, in which case approxima…
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…
Enhanced SMC: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals
Conor Rosato, Joshua Murphy, Alessandro Varsi +2
Sequential Monte Carlo Squared (SMC) is a Bayesian method which can infer the states and parameters of non-linear, non-Gaussian state-space models. The standard random-walk pro…
An SMC Algorithm on Distributed Memory with an Approx. Optimal L-Kernel
Conor Rosato, Alessandro Varsi, Joshua Murphy +1
Calibrating statistical models using Bayesian inference often requires both accurate and timely estimates of parameters of interest. Particle Markov Chain Monte Carlo (p-MCMC) and…