Zobrazeno 1 - 10
of 243
pro vyhledávání: '"Rougier, Jonathan"'
Computer models (simulators) are vital tools for investigating physical processes. Despite their utility, the prohibitive run-time of simulators hinders their direct application for uncertainty quantification. Gaussian process emulators (GPEs) have b
Externí odkaz:
http://arxiv.org/abs/2411.14005
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 distributed-processing technology. It is well known that modelling and pred
Externí odkaz:
http://arxiv.org/abs/1907.07813
Autor:
Rougier, Jonathan, Priebe, Carey
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 maximizing the evidence should appeal to both Bayesian and Frequentist s
Externí odkaz:
http://arxiv.org/abs/1906.11592
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 updating large-scale Gaussian process and modelling non-stationarity.
Externí odkaz:
http://arxiv.org/abs/1804.06285
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 through the Markov assumptions, and all operations in inference and predict
Externí odkaz:
http://arxiv.org/abs/1707.00892
Autor:
Rougier, Jonathan
Many applications require stochastic processes specified on two- or higher-dimensional domains; spatial or spatial-temporal modelling, for example. In these applications it is attractive, for conceptual simplicity and computational tractability, to p
Externí odkaz:
http://arxiv.org/abs/1702.05599
Autor:
Rougier, Jonathan
Some practical results are derived for population inference based on a sample, under the two qualitative conditions of 'ignorability' and exchangeability. These are the 'Histogram Theorem', for predicting the outcome of a non-sampled member of the po
Externí odkaz:
http://arxiv.org/abs/1511.03551
Autor:
Gollini, Isabella, Rougier, Jonathan
We consider the task of assessing the righthand tail of an insurer's loss distribution for some specified period, such as a year. We present and analyse six different approaches: four upper bounds, and two approximations. We examine these approaches
Externí odkaz:
http://arxiv.org/abs/1507.01853
Autor:
Lapins, Sacha, Roman, Diana C., Rougier, Jonathan, De Angelis, Silvio, Cashman, Katharine V., Kendall, J.-Michael
Publikováno v:
In Journal of Volcanology and Geothermal Research 1 January 2020 389
