12 papers
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…
Towards Scalable Proteomics: Opportunistic SMC Samplers on HTCondor
Matthew Carter, Lee Devlin, Alexander Philips +3
Quantitative proteomics plays a central role in uncovering regulatory mechanisms, identifying disease biomarkers, and guiding the development of precision therapies. These insights…
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…
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods
Conor Rosato, Harvinder Lehal, Simon Maskell +2
Bayesian inference with Markov Chain Monte Carlo (MCMC) is challenging when the likelihood function is irregular and expensive to compute. We explore several sampling algorithms th…
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…
An Entropic Metric for Measuring Calibration of Machine Learning Models
Daniel James Sumler, Lee Devlin, Simon Maskell +1
Understanding the confidence with which a machine learning model classifies an input datum is an important, and perhaps under-investigated, concept. In this paper, we propose a new…
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…