@article{reichert_white_bayarri_pitman_2011, title={Mechanism-based emulation of dynamic simulation models: Concept and application in hydrology}, volume={55}, ISSN={["1872-7352"]}, DOI={10.1016/j.csda.2010.10.011}, abstractNote={Many model-based investigation techniques, such as sensitivity analysis, optimization, and statistical inference, require a large number of model evaluations to be performed at different input and/or parameter values. This limits the application of these techniques to models that can be implemented in computationally efficient computer codes. Emulators, by providing efficient interpolation between outputs of deterministic simulation models, can considerably extend the field of applicability of such computationally demanding techniques. So far, the dominant techniques for developing emulators have been priors in the form of Gaussian stochastic processes (GASP) that were conditioned with a design data set of inputs and corresponding model outputs. In the context of dynamic models, this approach has two essential disadvantages: (i) these emulators do not consider our knowledge of the structure of the model, and (ii) they run into numerical difficulties if there are a large number of closely spaced input points as is often the case in the time dimension of dynamic models. To address both of these problems, a new concept of developing emulators for dynamic models is proposed. This concept is based on a prior that combines a simplified linear state space model of the temporal evolution of the dynamic model with Gaussian stochastic processes for the innovation terms as functions of model parameters and/or inputs. These innovation terms are intended to correct the error of the linear model at each output step. Conditioning this prior to the design data set is done by Kalman smoothing. This leads to an efficient emulator that, due to the consideration of our knowledge about dominant mechanisms built into the simulation model, can be expected to outperform purely statistical emulators at least in cases in which the design data set is small. The feasibility and potential difficulties of the proposed approach are demonstrated by the application to a simple hydrological model.}, number={4}, journal={COMPUTATIONAL STATISTICS & DATA ANALYSIS}, author={Reichert, P. and White, G. and Bayarri, M. J. and Pitman, E. B.}, year={2011}, month={Apr}, pages={1638–1655} }
 @article{white_ghosh_2009, title={A stochastic neighborhood conditional autoregressive model for spatial data}, volume={53}, ISSN={["1872-7352"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-62849092527&partnerID=MN8TOARS}, DOI={10.1016/j.csda.2008.08.010}, abstractNote={A spatial process observed over a lattice or a set of irregular regions is usually modeled using a conditionally autoregressive (CAR) model. The neighborhoods within a CAR model are generally formed deterministically using the inter-distances or boundaries between the regions. An extension of CAR model is proposed in this article where the selection of the neighborhood depends on unknown parameter(s). This extension is called a Stochastic Neighborhood CAR (SNCAR) model. The resulting model shows flexibility in accurately estimating covariance structures for data generated from a variety of spatial covariance models. Specific examples are illustrated using data generated from some common spatial covariance functions as well as real data concerning radioactive contamination of the soil in Switzerland after the Chernobyl accident.}, number={8}, journal={COMPUTATIONAL STATISTICS & DATA ANALYSIS}, author={White, Gentry and Ghosh, Sujit K.}, year={2009}, month={Jun}, pages={3033–3046} }