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Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach (2020)
Conference Proceeding
Cumming, J., Botsas, T., Jermyn, I., & Gringarten, A. (2020). Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach. In SPE Virtual Europec 2020 ; proceedings (SPE-200617-MS). https://doi.org/10.2118/200617-ms

Objectives/Scope: A stable, single-well deconvolution algorithm has been introduced for well test analysis in the early 2000’s, that allows to obtain information about the reservoir system not always available from individual flow periods, for exampl... Read More about Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach.

A Bayesian Optimisation Workflow for Field Development Planning Under Geological Uncertainty (2020)
Conference Proceeding
Bordas, R., Heritage, J., Javed, M., Peacock, G., Taha, T., Ward, P., …Hammersley, R. (2020). A Bayesian Optimisation Workflow for Field Development Planning Under Geological Uncertainty. . https://doi.org/10.3997/2214-4609.202035121

Field development planning using reservoir models is a key step in the field development process. Numerical optimisation of specific field development strategies is often used to aid planning. Bayesian Optimisation is a popular optimisation method th... Read More about A Bayesian Optimisation Workflow for Field Development Planning Under Geological Uncertainty.

Accounting for Model Discrepancy in Uncertainty Analysis by Combining Numerical Simulation and Bayesian Emulation Techniques (2020)
Conference Proceeding
Formentin, H. N., Vernon, I., Goldstein, M., Caiado, C., Avansi, G., & Schiozer, D. (2020). Accounting for Model Discrepancy in Uncertainty Analysis by Combining Numerical Simulation and Bayesian Emulation Techniques. . https://doi.org/10.3997/2214-4609.202035095

Model discrepancy specifies unavoidable differences between a physical system and its corresponding computer model. Incomplete information, simplifications and lack of knowledge about the physical state originate model discrepancy. Misevaluation of m... Read More about Accounting for Model Discrepancy in Uncertainty Analysis by Combining Numerical Simulation and Bayesian Emulation Techniques.

A Bayesian Statistical Approach to Decision Support for TNO OLYMPUS Well Control Optimisation under Uncertainty (2020)
Conference Proceeding
Owen, J., Vernon, I., & Hammersley, R. (2020). A Bayesian Statistical Approach to Decision Support for TNO OLYMPUS Well Control Optimisation under Uncertainty. . https://doi.org/10.3997/2214-4609.202035109

Well control and field development optimisation are tasks of increasing importance within the petroleum industry, as seen by the development of and large participation in the 2018 TNO OLYMPUS Field Development Optimisation Challenge. Complex mathemat... Read More about A Bayesian Statistical Approach to Decision Support for TNO OLYMPUS Well Control Optimisation under Uncertainty.

Topological terms in abelian lattice field theories (2020)
Conference Proceeding
Gattringer, C., Anosova, M., Göschl, D., Sulejmanpasic, T., & Törek, P. (2020). Topological terms in abelian lattice field theories. . https://doi.org/10.22323/1.363.0082

In this contribution we revisit the lattice discretization of the topological charge for abelian lattice field theories. The construction departs from an initially non-compact discretization of the gauge fields and after absorbing 2π shifts of the ga... Read More about Topological terms in abelian lattice field theories.

A sensitivity analysis and error bounds for the adaptive lasso (2020)
Conference Proceeding
Basu, T., Einbeck, J., & Troffaes, M. (2020). A sensitivity analysis and error bounds for the adaptive lasso. In I. Irigoien, D. -. Lee, J. Martinez-Minaya, & M. X. Rodriguez-Alvarez (Eds.), Proceedings of the 35th International Workshop on Statistical Modelling (278-281)

Sparse regression is an efficient statistical modelling technique which is of major relevance for high dimensional problems. There are several ways of achieving sparse regression, the well-known lasso being one of them. However, lasso variable select... Read More about A sensitivity analysis and error bounds for the adaptive lasso.

Phase separation and sharp large deviations (2020)
Conference Proceeding
Hryniv, O., & Wallace, C. (2020). Phase separation and sharp large deviations. In S. Poghosyan, M. Rafler, & S. Roelly (Eds.), Proceedings of the XI international conference Stochastic and Analytic Methods in Mathematical Physics (155-164). https://doi.org/10.25932/publishup-45919

Using a refined analysis of phase boundaries, we derive sharp asymptotics of the large deviation probabilities for the total magnetisation of a low-temperature Ising model in two dimensions.

Binary Credal Classification Under Sparsity Constraints (2020)
Conference Proceeding
Basu, T., Troffaes, M. C., & Einbeck, J. (2020). Binary Credal Classification Under Sparsity Constraints. In M. Lesot, S. Vieira, M. Z. Reformat, J. P. Carvalho, A. Wilbik, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information processing and management of uncertainty in knowledge-based systems : 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, proceedings, Part II (82-95). https://doi.org/10.1007/978-3-030-50143-3_7

Binary classification is a well known problem in statistics. Besides classical methods, several techniques such as the naive credal classifier (for categorical data) and imprecise logistic regression (for continuous data) have been proposed to handle... Read More about Binary Credal Classification Under Sparsity Constraints.