380 citations · 496 across the 21 of their papers we have counts for
25 papers
Bayesian Emulation of Geotechnical Deterioration Curves Using Quadratic and B-Spline Hierarchical Models
Jordan L. Oakley, Aleksandra Svalova, Peter Helm +4
The stability of geotechnical infrastructure assets, such as cuttings and embankments, is crucial to the safe and efficient delivery of transport services. The successful emulation…
Optimal combination of composite likelihoods using approximate Bayesian computation with application to state-space models
Wentao Li, Rosabeth White, Dennis Prangle
Composite likelihood provides approximate inference when the full likelihood is intractable and sub-likelihood functions of marginal events can be evaluated relatively easily. It h…
Measure Transport with Kernel Stein Discrepancy
Matthew A. Fisher, Tui Nolan, Matthew M. Graham +2
Measure transport underpins several recent algorithms for posterior approximation in the Bayesian context, wherein a transport map is sought to minimise the Kullback--Leibler diver…
Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners
Rob Geada, Dennis Prangle, Andrew Stephen McGough
One-shot Neural Architecture Search (NAS) aims to minimize the computational expense of discovering state-of-the-art models. However, in the past year attention has been drawn to t…
Distilling Importance Sampling for Likelihood Free Inference
Dennis Prangle, Cecilia Viscardi
Likelihood-free inference involves inferring parameter values given observed data and a simulator model. The simulator is computer code which takes parameters, performs stochastic…
The Neural Moving Average Model for Scalable Variational Inference of State Space Models
Tom Ryder, Dennis Prangle, Andrew Golightly +1
Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training. To date, however, this strategy has been most a…