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All Outputs (114)

Data-Driven Infrastructure Planning for Offshore Wind Farms (2024)
Presentation / Conference Contribution
Saxena, I., Kazemtabrizi, B., Troffaes, M. C., & Crabtree J., C. (2024, May). Data-Driven Infrastructure Planning for Offshore Wind Farms. Presented at Torque 2024, Florence, Italy

Offshore wind farms are one of the major renewable energy resources that can help the UK to reach its net zero target. Under the 10 point plan of the green revolution, the UK is set to quadruple its wind energy production by increasing its offshore w... Read More about Data-Driven Infrastructure Planning for Offshore Wind Farms.

Regret-based budgeted decision rules under severe uncertainty (2024)
Journal Article
Nakharutai, N., Destercke, S., & Troffaes, M. C. M. (2024). Regret-based budgeted decision rules under severe uncertainty. Information Sciences, 665, Article 120361. https://doi.org/10.1016/j.ins.2024.120361

One way to make decisions under uncertainty is to select an optimal option from a possible range of options, by maximizing the expected utilities derived from a probability model. However, under severe uncertainty, identifying preci... Read More about Regret-based budgeted decision rules under severe uncertainty.

A nonstandard approach to stochastic processes under probability bounding (2023)
Presentation / Conference Contribution
Troffaes, M. C. M. (2023). A nonstandard approach to stochastic processes under probability bounding. In E. Miranda, I. Montes, E. Quaeghebeur, & B. Vantaggi (Eds.), Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications (450-460)

Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning (2023)
Journal Article
Rajabdorri, M., Kazemtabrizi, B., Troffaes, M., Sigrist, L., & Lubato, E. (2023). Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning. Sustainable Energy, Grids and Networks, 36, Article 101161. https://doi.org/10.1016/j.segan.2023.101161

As the intention is to reduce the amount of thermal generation and to increase the share of clean energy, power systems are increasingly becoming susceptible to frequency instability after outages due to reduced levels of inertia. To address this iss... Read More about Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning.

Decision making under severe uncertainty on a budget (2022)
Presentation / Conference Contribution
Nakharutai, N., Destercke, S., & Troffaes, M. C. (2022, October). Decision making under severe uncertainty on a budget. Presented at Scalable Uncertainty Management (SUM 2022), Paris, France

Convex sets of probabilities are general models to describe and reason with uncertainty. Moreover, robust decision rules defined for them enable one to make cautious inferences by allowing sets of optimal actions to be returned, reflecting lack of in... Read More about Decision making under severe uncertainty on a budget.

Foundations for temporal reasoning using lower previsions without a possibility space (2022)
Book Chapter
Troffaes, M. C., & Goldstein, M. (2022). Foundations for temporal reasoning using lower previsions without a possibility space. In T. Augustin, F. Gagliardi Cozman, & G. Wheeler (Eds.), Reflections on the Foundations of Probability and Statistics: Essays in Honor of Teddy Seidenfeld (69-96). (1). Springer Verlag. https://doi.org/10.1007/978-3-031-15436-2_4

We introduce a new formal mathematical framework for probability theory, taking random quantities to be the fundamental objects of interest, without reference to a possibility space, in spirit of de Finetti’s treatment of probability, Goldstein’s Bay... Read More about Foundations for temporal reasoning using lower previsions without a possibility space.

Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis (2022)
Journal Article
Raices Cruz, I., Lindström, J., Troffaes, M. C., & Sahlin, U. (2022). Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis. Computational Statistics & Data Analysis, 176, Article 107558. https://doi.org/10.1016/j.csda.2022.107558

Bayesian inference under a set of priors, called robust Bayesian analysis, allows for estimation of parameters within a model and quantification of epistemic uncertainty in quantities of interest by bounded (or imprecise) probability. Iterative impor... Read More about Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis.

A robust Bayesian analysis of variable selection under prior ignorance (2022)
Journal Article
Basu, T., Troffaes, M. C., & Einbeck, J. (2023). A robust Bayesian analysis of variable selection under prior ignorance. Sankhya A - Mathematical Statistics and Probability, 85(1), 1014-1057. https://doi.org/10.1007/s13171-022-00287-2

We propose a cautious Bayesian variable selection routine by investigating the sensitivity of a hierarchical model, where the regression coefficients are specified by spike and slab priors. We exploit the use of latent variables to understand the imp... Read More about A robust Bayesian analysis of variable selection under prior ignorance.

A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis (2022)
Journal Article
Raices Cruz, I., Troffaes, M. C., Lindström, J., & Sahlin, U. (2022). A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis. Statistics in Medicine, 41(17), 3365-3379. https://doi.org/10.1002/sim.9422

Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network mo... Read More about A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis.

A suggestion for the quantification of precise and bounded probability to quantify epistemic uncertainty in scientific assessments (2022)
Journal Article
Raices Cruz, I., Troffaes, M., & Sahlin, U. (2022). A suggestion for the quantification of precise and bounded probability to quantify epistemic uncertainty in scientific assessments. Risk Analysis, 42(2), 239-253. https://doi.org/10.1111/risa.13871

An honest communication of uncertainty about quantities of interest enhances transparency in scientific assessments. To support this communication, risk assessors should choose appropriate ways to evaluate and characterize epistemic uncertainty. A fu... Read More about A suggestion for the quantification of precise and bounded probability to quantify epistemic uncertainty in scientific assessments.

Inclusion of Frequency Stability Constraints in Unit Commitment Using Separable Programming (2021)
Journal Article
Ferrandon-Cervantes, C., Kazemtabrizi, B., & Troffaes, M. (2022). Inclusion of Frequency Stability Constraints in Unit Commitment Using Separable Programming. Electric Power Systems Research, 203, Article 107669. https://doi.org/10.1016/j.epsr.2021.107669

In this paper we address the problem of frequency stability in the unit commitment (UC) optimisation process. We include a set of appropriately defined frequency stability constraints in the UC problem formulation for operational planning scenarios i... Read More about Inclusion of Frequency Stability Constraints in Unit Commitment Using Separable Programming.

Bayesian Adaptive Selection Under Prior Ignorance (2021)
Presentation / Conference Contribution
Basu, T., Troffaes, M. C., & Einbeck, J. (2021). Bayesian Adaptive Selection Under Prior Ignorance. In M. Vasile, & D. Quagliarella (Eds.), . https://doi.org/10.1007/978-3-030-80542-5_22

Bayesian variable selection is one of the popular topics in modern day statistics. It is an important tool for high dimensional statistics, where the number of model parameters is greater than the number of observations. Several Bayesian models have... Read More about Bayesian Adaptive Selection Under Prior Ignorance.

Robust decision analysis under severe uncertainty and ambiguous tradeoffs: an invasive species case study (2021)
Journal Article
Sahlin, U., Troffaes, M. C., & Edsman, L. (2021). Robust decision analysis under severe uncertainty and ambiguous tradeoffs: an invasive species case study. Risk Analysis, 41(11), 2140-2153. https://doi.org/10.1111/risa.13722

Bayesian decision analysis is a useful method for risk management decisions, but is limited in its ability to consider severe uncertainty in knowledge, and value ambiguity in management objectives. We study the use of robust Bayesian decision analysi... Read More about Robust decision analysis under severe uncertainty and ambiguous tradeoffs: an invasive species case study.

Improving and benchmarking of algorithms for Γ-maximin, Γ-maximax and interval dominance (2021)
Journal Article
Nakharutai, N., Troffaes, M. C., & Caiado, C. C. (2021). Improving and benchmarking of algorithms for Γ-maximin, Γ-maximax and interval dominance. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 133, 95-115. https://doi.org/10.1016/j.ijar.2021.03.005

Γ-maximin, Γ-maximax and interval dominance are familiar decision criteria for making decisions under severe uncertainty, when probability distributions can only be partially identified. One can apply these three criteria by solving sequences of line... Read More about Improving and benchmarking of algorithms for Γ-maximin, Γ-maximax and interval dominance.

Modelling of modular battery systems under cell capacity variation and degradation (2020)
Journal Article
Rogers, D. J., Aslett, L. J., & Troffaes, M. C. (2021). Modelling of modular battery systems under cell capacity variation and degradation. Applied Energy, 43, Article 116360. https://doi.org/10.1016/j.apenergy.2020.116360

We propose a simple statistical model of electrochemical cell degradation based on the general characteristics observed in previous large-scale experimental studies of cell degradation. This model is used to statistically explore the behaviour and li... Read More about Modelling of modular battery systems under cell capacity variation and degradation.

A sensitivity analysis and error bounds for the adaptive lasso (2020)
Presentation / Conference Contribution
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.

An Economic Model for Offshore Transmission Asset Planning Under Severe Uncertainty (2020)
Journal Article
Bains, H., Madariaga, A., Troffaes, M. C., & Kazemtabrizi, B. (2020). An Economic Model for Offshore Transmission Asset Planning Under Severe Uncertainty. Renewable Energy, 160, 1174-1184. https://doi.org/10.1016/j.renene.2020.05.160

The inherent uncertainties associated with offshore wind are substantial, as are the investments. Therefore, investors are keen to identify and evaluate the risks. This paper presents a model to economically evaluate projects from an offshore transmi... Read More about An Economic Model for Offshore Transmission Asset Planning Under Severe Uncertainty.