380 citations · 678 across the 56 of their papers we have counts for
62 papers
Predictive variational inference for flexible regression models
Lucas Kock, Scott A. Sisson, G. S. Rodrigues +1
A conventional Bayesian approach to prediction uses the posterior distribution to integrate out parameters in a density for unobserved data conditional on the observed data and par…
Compact Bayesian Neural Networks via pruned MCMC sampling
Ratneel Deo, Scott Sisson, Jody M. Webster +1
Bayesian Neural Networks (BNNs) offer robust uncertainty quantification in model predictions, but training them presents a significant computational challenge. This is mainly due t…
Calibrated Multivariate Regression with Localized PIT Mappings
Lucas Kock, G. S. Rodrigues, Scott A. Sisson +2
Calibration ensures that predicted uncertainties align with observed uncertainties. While there is an extensive literature on recalibration methods for univariate probabilistic for…
Positional Encoder Graph Quantile Neural Networks for Geographic Data
William E. R. de Amorim, Scott A. Sisson, T. Rodrigues +2
Positional Encoder Graph Neural Networks (PE-GNNs) are among the most effective models for learning from continuous spatial data. However, their predictive distributions are often…
Analysing symbolic data by pseudo-marginal methods
Yu Yang, Matias Quiroz, Boris Beranger +2
Symbolic data analysis (SDA) aggregates large individual-level datasets into a small number of distributional summaries, such as random rectangles or random histograms. The inferen…
Fast and flexible inference for spatial extremes
Peng Zhong, Scott A. Sisson, Boris Beranger
Statistical modelling of spatial extreme events has gained increasing attention over the last few decades with max-stable processes, and more recently -Pareto processes, becomin…