We construct non-Gaussian processes that vary continuously in space and time with nonseparable covariance functions. Starting from a general and flexible way of constructing valid nonseparable covariance functions through mixing over separable covariance functions, the resulting models are generalized by allowing for outliers as well as regions with larger variances. We induce this through scale mixing with separate positive-valued processes. Smooth mixing processes are applied to the underlying correlated processes in space and in time, thus leading to regions in space and time of increased spread. An uncorrelated mixing process on the nugget effect accommodates outliers. Posterior and predictive Bayesian inference with these models is implemented through a Markov chain Monte Carlo sampler. An application to temperature data in the Basque country illustrates the potential of this model in the identification of outliers and regions with inflated variance, and shows that this improves the predictive performance.

Preservation of digital material is a challenge for which many archives feel underprepared and ill equipped. The National Archives (UK) has been working in collaboration with statisticians from the University of Warwick and partners from across the UK archives sector to develop a decision-support system which quantifies the risks involved in digital preservation. Through interdisciplinary collaboration, this partnership has developed an interactive tool for managing risks to digital material, based on a Bayesian statistical network. The tool provides archivists with a different way of thinking about digital preservation, supported by an evidence base they can use to advocate for action. The project illustrates the potential benefit of a collaborative approach, combining insight from different disciplines.