50 citations · 248 across the 29 of their papers we have counts for
4 papers · 1 filter
Spatial Bayesian Neural Networks
Andrew Zammit-Mangion, Michael D. Kaminski, Ba-Hien Tran +2
interpretable, and well understood models that are routinely employed even though, as is revealed through prior and posterior predictive checks, these can poorly characterise the s…
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Jordan Richards +1
Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner. Although they are appealing to use with spatial models, where es…
Neural Bayes estimators for censored inference with peaks-over-threshold models
Jordan Richards, Matthew Sainsbury-Dale, Andrew Zammit-Mangion +1
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances i…
Adaptive Spatial Sampling Design for Environmental Field Prediction using Low-Cost Sensing Technologies
Eun-Hye Yoo, Andrew Zammit-Mangion, Michael G. Chipeta
The last decade has seen an explosion in data sources available for the monitoring and prediction of environmental phenomena. While several inferential methods have been developed…