Bayesian methods for complex statistical applications

Flexible, scalable, exact and approximate, Bayesian posterior simulation methods are desirable to solve real-world problems in areas, such as economic inequality and poverty measurement, health, economics, cognitive psychology, and finance. 

Exceedances and their uncertainty quantification

A compelling reason for environmental monitoring, and the purpose of many environmental studies, is to predict the location and the extent of extreme events, such as floods and drought, and to quantify the probability of such an event. Whilst statistical models are routinely used for spatial prediction, exceedance probabilities for spatial extremes are not as straightforward: Dependent spatial predictions at many locations must be assessed simultaneously, and it is often the extremes of spatially aggregated regions that is of interest. Important applications of this work include predicting the locations of carbon dioxide sources and sinks, and predicting the regions that may be affected by global climate change. 

Reproducibility and visualisation in environmental modelling and inference

In the digital age, when data and algorithms are implemented in a computer, reproducibility has never been easier. However, many researchers still consider reproducibility as an `afterthought'. At CEI we spend a considerable amount of time exploring ways in which to make our results reproducible through versioning and packaging. We outline the protocols that we have found most useful in ensuring permanence of the results. After ensuring reproducibility, we then explore ways the results can be disseminated, visually, to scientists and members of the general public. (Image credit: C.K. Wikle)