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Professor Matthias Troffaes' Outputs (119)

A robust Bayesian model to quantify and adjust for study quality and conflict of interest in meta-analyses (2025)
Presentation / Conference Contribution
Troffaes, M. C. M., Casini, L., Landes, J., & Sahlin, U. (2025, July). A robust Bayesian model to quantify and adjust for study quality and conflict of interest in meta-analyses. Presented at 14th International Symposium on Imprecise Probabilities: Theories and Applications, Bielefeld, Germany

Meta-analyses are vital for synthesizing evidence in medical research, but conflicts of interest can introduce research bias, undermining the reliability of the synthesized findings. This paper proposes a new robust Bayesian meta-analysis model. The... Read More about A robust Bayesian model to quantify and adjust for study quality and conflict of interest in meta-analyses.

Robust Bayesian causal estimation for causal inference in medical diagnosis (2024)
Journal Article
Basu, T., & Troffaes, M. C. M. (2025). Robust Bayesian causal estimation for causal inference in medical diagnosis. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 177, Article 109330. https://doi.org/10.1016/j.ijar.2024.109330

Causal effect estimation is a critical task in statistical learning that aims to find the causal effect on subjects by identifying causal links between a number of predictor (or, explanatory) variables and the outcome of a treatment... Read More about Robust Bayesian causal estimation for causal inference in medical diagnosis.

Data-driven estimation of the amount of under frequency load shedding in small power systems (2024)
Journal Article
Rajabdorri, M., Troffaes, M. C. M., Kazemtabrizi, B., Sarvarizadeh, M., Sigrist, L., & Lobato, E. (2025). Data-driven estimation of the amount of under frequency load shedding in small power systems. Engineering Applications of Artificial Intelligence, 139(Part B), Article 109617. https://doi.org/10.1016/j.engappai.2024.109617

This paper presents a data-driven methodology for estimating under frequency load shedding (UFLS) in small power systems. UFLS plays a vital role in maintaining system stability by shedding load when the frequency drops below a specified threshold fo... Read More about Data-driven estimation of the amount of under frequency load shedding in small power systems.

Elicitation for decision problems under severe uncertainties (2024)
Presentation / Conference Contribution
Nakharutai, N., Troffaes, M., & Destercke, S. (2024, November). Elicitation for decision problems under severe uncertainties. Presented at The 16th International Conference on Scalable Uncertainty Management (SUM 2024), Palermo, Italy

In this paper, we investigate the problem of eliciting information from an expert, where the assumed uncertainty model is a coherent upper prevision (or equivalently a closed convex set of probabilities). The goal is to solve a decision problem under... Read More about Elicitation for decision problems under severe uncertainties.

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 Robust Bayesian Approach for Causal Inference Problems (2023)
Presentation / Conference Contribution
Basu, T., Troffaes, M. C. M., & Einbeck, J. (2023, September). A Robust Bayesian Approach for Causal Inference Problems. Presented at ECSQARU 2023, Arras, France

Causal inference concerns finding the treatment effect on subjects along with causal links between the variables and the outcome. However, the underlying heterogeneity between subjects makes the problem practically unsolvable. Additionally, we often... Read More about A Robust Bayesian Approach for Causal Inference Problems.

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, December). Bayesian Adaptive Selection Under Prior Ignorance. Presented at UQOP 2020

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.