42 citations · 56 across the 7 of their papers we have counts for
9 papers
Multi-Scale Process Modelling and Distributed Computation for Spatial Data
Andrew Zammit-Mangion, Jonathan Rougier
Recent years have seen a huge development in spatial modelling and prediction methodology, driven by the increased availability of remote-sensing data and the reduced cost of distr…
The exact form of the 'Ockham factor' in model selection
Jonathan Rougier, Carey Priebe
We explore the arguments for maximizing the `evidence' as an algorithm for model selection. We show, using a new definition of model complexity which we term `flexibility', that ma…
Bayesian model-data synthesis with an application to global Glacio-Isostatic Adjustment
Zhe Sha, Jonathan Rougier, Maike Schumacher +1
We introduce a framework for updating large scale geospatial processes using a model-data synthesis method based on Bayesian hierarchical modelling. Two major challenges come from…
A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields
Andrew Zammit-Mangion, Jonathan Rougier
Gaussian Markov random fields are used in a large number of disciplines in machine vision and spatial statistics. The models take advantage of sparsity in matrices introduced throu…
A representation theorem for stochastic processes with separable covariance functions, and its implications for emulation
Jonathan Rougier
Many applications require stochastic processes specified on two- or higher-dimensional domains; spatial or spatial-temporal modelling, for example. In these applications it is attr…
Parametric uncertainty in complex environmental models: a cheap emulation approach for models with high-dimensional output
B. Swallow, M. Rigby, J. C. Rougier +3
In order to understand underlying processes governing environmental and physical processes, and predict future outcomes, a complex computer model is frequently required to simulate…