Outputs (51)

Emulation and History Matching using the hmer Package (2024)
Journal Article
Iskauskas, A., Vernon, I., Goldstein, M., Scarponi, D., McKinley, T. J., White, R. G., & McCreesh, N. (2024). Emulation and History Matching using the hmer Package. Journal of Statistical Software, 109(10), 1–48. https://doi.org/10.18637/jss.v109.i10

Modeling complex real-world situations such as infectious diseases, geological phenomena, and biological processes can present a dilemma: the computer model (referred to as a simulator) needs to be complex enough to capture the dynamics of the system... Read More about Emulation and History Matching using the hmer Package.

Demonstrating multi-country calibration of a tuberculosis model using new history matching and emulation package - hmer (2023)
Journal Article
Scarponi, D., Iskauskas, A., Clark, R. A., Vernon, I., McKinley, T. J., Goldstein, M., …McCreesh, N. (2023). Demonstrating multi-country calibration of a tuberculosis model using new history matching and emulation package - hmer. Epidemics, 43, Article 100678. https://doi.org/10.1016/j.epidem.2023.100678

Infectious disease models are widely used by epidemiologists to improve the understanding of transmission dynamics and disease natural history, and to predict the possible effects of interventions. As the complexity of such models increases, however,... Read More about Demonstrating multi-country calibration of a tuberculosis model using new history matching and emulation package - hmer.

Complex model calibration through emulation, a worked example for a stochastic epidemic model (2022)
Journal Article
Dunne, M., Mohammadi, H., Challenor, P., Borgo, R., Porphyre, T., Vernon, I., …Swallow, B. (2022). Complex model calibration through emulation, a worked example for a stochastic epidemic model. Epidemics, 39, Article 100574. https://doi.org/10.1016/j.epidem.2022.100574

Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemio... Read More about Complex model calibration through emulation, a worked example for a stochastic epidemic model.

Varying Coefficient Models and Design Choice for Bayes Linear Emulation of Complex Computer Models with Limited Model Evaluations (2022)
Journal Article
Wilson, A. L., Goldstein, M., & Dent, C. J. (2022). Varying Coefficient Models and Design Choice for Bayes Linear Emulation of Complex Computer Models with Limited Model Evaluations. SIAM/ASA Journal on Uncertainty Quantification, 10(1), 350-378. https://doi.org/10.1137/20m1318560

Computer models are widely used to help make decisions about real-world systems. As computer models of large and complex systems can have long run-times and high-dimensional input spaces, it is often necessary to use emulation to assess uncertainties... Read More about Varying Coefficient Models and Design Choice for Bayes Linear Emulation of Complex Computer Models with Limited Model Evaluations.

Intermediate Variable Emulation: using internal processes in simulators to build more informative emulators (2022)
Journal Article
Oughton, R., Goldstein, M., & Hemmings, J. (2022). Intermediate Variable Emulation: using internal processes in simulators to build more informative emulators. SIAM/ASA Journal on Uncertainty Quantification, 10(1), 268-293. https://doi.org/10.1137/20m1370902

Complex systems are often modelled by intricate and intensive computer simulators. This makes their behaviour difficult to study, and so a statistical representation of the simulator is often used, known as an emulator, to enable users to explore the... Read More about Intermediate Variable Emulation: using internal processes in simulators to build more informative emulators.

Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling (2022)
Journal Article
Swallow, B., Birrell, P., Blake, J., Burgman, M., Challenor, P., Coffeng, L. E., …Vernon, I. (2022). Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling. Epidemics, 38, https://doi.org/10.1016/j.epidem.2022.100547

The estimation of parameters and model structure for informing infectious disease response has become a focal point of the recent pandemic. However, it has also highlighted a plethora of challenges remaining in the fast and robust extraction of infor... Read More about Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.

Optimization via Statistical Emulation and Uncertainty Quantification: Hosting Capacity Analysis of Distribution Networks (2021)
Journal Article
Du, H., Sun, W., Goldstein, M., & Harrison, G. (2021). Optimization via Statistical Emulation and Uncertainty Quantification: Hosting Capacity Analysis of Distribution Networks. IEEE Access, 9, 118472-118483. https://doi.org/10.1109/access.2021.3105935

In power systems modelling, optimization methods based on certain objective function(s) are widely used to provide solutions for decision makers. For complex high-dimensional problems, such as network hosting capacity evaluation of intermittent renew... Read More about Optimization via Statistical Emulation and Uncertainty Quantification: Hosting Capacity Analysis of Distribution Networks.

Bayes linear analysis for ordinary differential equations (2021)
Journal Article
Jones, M., Goldstein, M., Randell, D., & Jonathan, P. (2021). Bayes linear analysis for ordinary differential equations. Computational Statistics & Data Analysis, 161, Article 107228. https://doi.org/10.1016/j.csda.2021.107228

Differential equation models are used in a wide variety of scientific fields to describe the behaviour of physical systems. Commonly, solutions to given systems of differential equations are not available in closed-form; in such situations, the solut... Read More about Bayes linear analysis for ordinary differential equations.

Efficient Selection of Reservoir Model Outputs within an Emulation-Based Bayesian History Matching Uncertainty Analysis (2019)
Journal Article
Ferreira, C., Vernon, I., Caiado, C., Formentin, H., Avansi, G., Goldstein, M., & Schiozer, D. (2019). Efficient Selection of Reservoir Model Outputs within an Emulation-Based Bayesian History Matching Uncertainty Analysis. SPE Journal, OTC-29801. https://doi.org/10.4043/29801-ms

When performing classic uncertainty reduction based on dynamic data, a large number of reservoir simulations need to be evaluated at high computational cost. As an alternative, we construct Bayesian emulators that mimic the dominant behaviour of the... Read More about Efficient Selection of Reservoir Model Outputs within an Emulation-Based Bayesian History Matching Uncertainty Analysis.

Gaining more understanding about reservoir behavior through assimilation of breakthrough time and productivity deviation in the history matching process (2019)
Journal Article
Formentin, H. N., Almeida, F. L. R., Avansi, G. D., Maschio, C., Schiozer, D. J., Caiado, C., …Goldstein, M. (2019). Gaining more understanding about reservoir behavior through assimilation of breakthrough time and productivity deviation in the history matching process. Journal of Petroleum Science and Engineering, 173, 1080-1096. https://doi.org/10.1016/j.petrol.2018.10.045

History matching (HM) is an inverse problem where uncertainties in attributes are reduced by comparison with observed dynamic data. Typically, normalized misfit summarizes dissimilarities between observed and simulation data. Especially for long-time... Read More about Gaining more understanding about reservoir behavior through assimilation of breakthrough time and productivity deviation in the history matching process.