55 citations · 121 across the 24 of their papers we have counts for
6 papers · 1 filter
Robust simulation design for generalized linear models in conditions of heteroscedasticity or correlation
Andrew Gill, David J. Warne, Antony M. Overstall +2
A meta-model of the input-output data of a computationally expensive simulation is often employed for prediction, optimization, or sensitivity analysis purposes. Fitting is enabled…
Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
Joshua J. Bon, Adam Bretherton, Katie Buchhorn +12
Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most sta…
Model-robust Bayesian design through Generalised Additive Models for monitoring submerged shoals
Dilishiya De Silva, Rebecca Fisher, Ben Radford +2
Optimal sampling strategies are critical for surveys of deeper coral reef and shoal systems, due to the significant cost of accessing and field sampling these remote and poorly und…
A model robust sub-sampling approach for Generalised Linear Models in Big data settings
Amalan Mahendran, Helen Thompson, James M. McGree
In today's modern era of Big data, computationally efficient and scalable methods are needed to support timely insights and informed decision making. One such method is sub-samplin…
Bayesian Design with Sampling Windows for Complex Spatial Processes
Katie Buchhorn, Kerrie Mengersen, Edgar Santos-Fernandez +2
Optimal design facilitates intelligent data collection. In this paper, we introduce a fully Bayesian design approach for spatial processes with complex covariance structures, like…
SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks
Edgar Santos-Fernandez, Jay M. Ver Hoef, James M. McGree +3
Spatio-temporal models are widely used in many research areas from ecology to epidemiology. However, most covariance functions describe spatial relationships based on Euclidean dis…