4 papers
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…
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…
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…