Staff profile

After receiving his MSc degree in engineering (theoretical physics) in 2000 from Gent University, Belgium, Matthias Troffaes joined the SYSTeMS research group at the same university as a doctoral researcher, pursuing research in imprecise probability theory under the guidance of Gert de Cooman, earning the degree of PhD in April 2005. In July 2005, he went to Carnegie Mellon University as a Francqui Foundation Fellow of the Belgian American Educational Foundation, working as a post-doctoral researcher with Teddy Seidenfeld. In September 2006, he joined the Department of Mathematical Sciences, Durham University, where he is currently Professor.

Research

My research is concerned with modelling and quantifying severe uncertainty and decision making under severe uncertainty. My interests lie in the theoretical probabilistic foundations behind such modelling, as well as practical statistical applications of such modelling.

In many practical applications, severe uncertainty arises due to insufficient data or expert opinion, relative to the complexity of the model. In such cases, it has been argued that no unique probability distribution can really honestly describe our knowledge. This discussion has a long history; see for instance Boole (1854), Keynes (1921), Williams (1975), Walley (1991), Weichselberger (1995), Shafer & Vovk (2001), Troffaes & De Cooman (2014), and many others.

For example, consider the probability that it rains on the day exactly twenty years from now. Experts may have a hard time to put a precise number on such probability, due to the complexity of climate modelling, but also due to uncertainty about climate change and about how politicians will respond it. However, experts may find it much easier to specify lower and upper bounds on such probability. From a Bayesian point of view, experts may have a hard time to put a precise prior distribution over such probability, but they may find it much easier to specify a set of prior distributions, for instance by bounding prior predictive quantities.

My work focuses on the mathematical theory for propagating probability bounds through models and through decision problems. I also look at practical statistical applications of such theories, mostly in engineering and in environmental sciences.

• decision making
• foundations of probability and statistics
• uncertainty modelling
• risk
• severe uncertainty
• imprecise probability
• engineering
• renewable energy
• environment

• 2019: Secretary of SIPTA (Society for Imprecise Probability: Theories and Applications): I acted as secretary of the Society for Imprecise Probability: Theories and Applications (SIPTA). This society promotes research on imprecise probability through a series of activities. These activities bring together researchers from different groups, create resources for information, dissemination and documentation, and make other people aware of the potential of imprecise probability models.
• 2015: President of SIPTA (Society for Imprecise Probability: Theories and Applications): From July 2015 until June 2017, I have been president of the Society for Imprecise Probability: Theories and Applications (SIPTA).

Authored book

• Troffaes, M. C., & De Cooman, G. (2014). Lower previsions. Wiley

Chapter in book

• 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
• Basu, T., Einbeck, J., & Troffaes, M. C. (2021). Uncertainty Quantification in Lasso-Type Regularization Problems. In Optimization Under Uncertainty with Applications to Aerospace Engineering (81-109). Springer Verlag. https://doi.org/10.1007/978-3-030-60166-9_3
• Troffaes, M. C., & Hable, R. (2014). Computation. In T. Augustin, F. P. Coolen, G. De Cooman, & M. C. Troffaes (Eds.), Introduction to Imprecise Probabilities (329-337). Wiley. https://doi.org/10.1002/9781118763117.ch16
• Huntley, N., Hable, R., & Troffaes, M. C. (2014). Decision making. In T. Augustin, F. P. Coolen, G. de Cooman, & M. C. Troffaes (Eds.), Introduction to Imprecise Probabilities (190-206). Wiley. https://doi.org/10.1002/9781118763117.ch8
• Coolen, F. P., Troffaes, M. C., & Augustin, T. (2011). Imprecise probability. In M. Lovric (Ed.), International Encyclopedia of Statistical Science. Springer Verlag
• Coolen-Schrijner, P., Coolen, F. P., Troffaes, M. C., & Augustin, T. (2009). Imprecision in Statistical Theory and Practice. In P. Coolen-Schrijner, F. P. Coolen, M. C. Troffaes, & T. Augustin (Eds.), Imprecision in Statistical Theory and Practice. Grace Scientific Publishing
• Troffaes, M. C. (2006). Efficient and Robust Global Amino Acid Sequence Alignment with Uncertain Evolutionary Distances. In B. Bouchon-Meunier, G. Coletti, & R. Yager (Eds.), Modern Information Processing: From Theory to Applications (371-381). Elsevier
• Troffaes, M. C., & De Cooman, G. (2003). Extension of coherent lower previsions to unbounded random variables. In B. Bouchon-Meunier, L. Foulloy, & R. Yager (Eds.), Intelligent Systems for Information Processing: From Representation to Applications (277-288). North-Holland. https://doi.org/10.1016/b978-044451379-3/50023-6

Conference Paper

• 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)
• Nakharutai, N., Destercke, S., & Troffaes, M. C. (2022). Decision making under severe uncertainty on a budget. In F. Dupin de Saint-Cyr, M. Öztürk-Escoffier, & N. Potyka (Eds.), . https://doi.org/10.1007/978-3-031-18843-5_13
• 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
• 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)
• 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
• Troffaes, M. C., & Basu, T. (2019). A Cantelli-type inequality for constructing non-parametric p-boxes based on exchangeability. In J. D. Bock, C. P. . D. Campos, G. D. Cooman, E. Quaeghebeur, & G. Wheeler (Eds.), Proceedings of the Eleventh International Symposium on Imprecise Probabilities : Theories and Applications (386-393)
• Troffaes, M. C., Krak, T., & Bains, H. (2019). Two-State Imprecise Markov Chains for Statistical Modelling of Two-State Non-Markovian Processes. In J. De Bock, C. P. de Campos, G. de Cooman, E. Quaeghebeur, & G. Wheeler (Eds.), Proceedings of the Eleventh International Symposium on Imprecise Probabilities : Theories and Applications (394-403)
• Bains, H., Madariaga, A., Kazemtabrizi, B., & Troffaes, M. C. (2019). The Impact of Offshore Transmission Regulatory Regimes on Technology Choices. In Proceedings of the Cigre Symposium Aalborg, 2019
• Cervantes, C., Kazemtabrizi, B., & Troffaes, M. (2018). Contingency Ranking in Power Systems via Reliability Rates. In 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) : 12-15 June, 2018, Palermo, Italy . Conference proceedings. https://doi.org/10.1109/eeeic.2018.8493853
• Troffaes, M. C., Fetz, T., & Oberguggenberger, M. (2018). Iterative Importance Sampling for Estimating Expectation Bounds Under Partial Probability Specifications.
• Heylen, E., Deconinck, G., Van Hertem, D., Troffaes, M. C., & Kazemtabrizi, B. (2017). Qualitative comparison of techniques for evaluating performance of short term power system reliability management. In 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) : Torino, Italy, 26-29 September 2017 : conference proceedings (333-339). https://doi.org/10.1109/isgteurope.2017.8260148
• Troffaes, M. C. (2017). A note on imprecise Monte Carlo over credal sets via importance sampling. In A. Antonucci, G. Corani, I. Couso, & S. Destercke (Eds.), Proceedings of the Tenth International Symposium on Imprecise Probability : Theories and Applications, 10-14 July 2017, Lugano (Switzerland) (325-332)
• Troffaes, M. C., & Sahlin, U. (2017). Imprecise swing weighting for multi-attribute utility elicitation based on partial preferences. In A. Antonucci, G. Corani, I. Couso, & S. Destercke (Eds.), Proceedings of the Tenth International Symposium on Imprecise Probability : Theories and Applications, 10-14 July 2017, Lugano (Switzerland) (333-345)
• Nakharutai, N., Troffaes, M. C., & Caiado, C. C. (2017). Efficient algorithms for checking avoiding sure loss. In A. Antonucci, G. Corani, I. Couso, & S. Destercke (Eds.), Proceedings of the Tenth International Symposium on Imprecise Probability : Theories and Applications, 10-14 July 2017, Lugano (Switzerland) (241-252)
• Sheehy, S., Edwards, G., Dent, C., Kazemtabrizi, B., Troffaes, M., & Tindemans, S. (2016). Impact of high wind penetration on variability of unserved energy in power system adequacy. In 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) : Beijing, China, 16-20 October 2016 ; proceedings (1-6). https://doi.org/10.1109/pmaps.2016.7764199
• Troffaes, M. C., Williams, E., & Dent, C. J. (2015). Data Analysis and Robust Modelling of the Impact of Renewable Generation on Long Term Security of Supply and Demand. In 2015 IEEE Power and Energy Society General Meeting (1-5). https://doi.org/10.1109/pesgm.2015.7286070
• Paton, L., Troffaes, M. C., Boatman, N., Hussein, M., & Hart, A. (2015). A robust Bayesian analysis of the impact of policy decisions on crop rotations. In T. Augustin, S. Doria, E. Miranda, & E. Quaeghebeur (Eds.), ISIPTA ’15 : proceedings of the 9th International Symposium on Imprecise Probability : Theories and Applications, 20-24 July 2015, Pescara, Italy (217-226)
• Troffaes, M., Gledhill, J., Škulj, D., & Blake, S. (2015). Using imprecise continuous time Markov chains for assessing the reliability of power networks with common cause failure and non-immediate repair. In T. Augustin, S. Doria, E. Miranda, & E. Quaeghebeur (Eds.), ISIPTA ’15 : proceedings of the 9th International Symposium on Imprecise Probability : Theories and Applications, 20-24 July 2015, Pescara, Italy (287-294)
• Troffaes, M. C., Coolen, F. P., & Destercke, S. (2014). A Note on Learning Dependence Under Severe Uncertainty. In Information processing and management of uncertainty in knowledge-based systems : 15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014 ; proceedings, part III (498-507). https://doi.org/10.1007/978-3-319-08852-5_51
• Paton, L., Troffaes, M. C., Boatman, N., Hussein, M., & Hart, A. (2014). Multinomial logistic regression on Markov chains for crop rotation modelling. In Information processing and management of uncertainty in knowledge-based systems : 15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014 ; proceedings, part III (476-485). https://doi.org/10.1007/978-3-319-08852-5_49
• Troffaes, M. C., & Paton, L. (2013). Logistic Regression on Markov Chains for Crop Rotation Modelling. In F. Cozman, T. Denoeux, S. Destercke, & T. Seidenfeld (Eds.), ISIPTA ’13 : proceedings of the eighth international symposium on imprecise probability : theories and applications July 2-5 2013, Compiègne, France (329-336)
• Troffaes, M. C., & Blake, S. (2013). A Robust Data Driven Approach to Quantifying Common-Cause Failure in Power Networks. In F. Cozman, T. Denoeux, S. Destercke, & T. Seidenfeld (Eds.), ISIPTA ’13 : proceedings of the eighth international symposium on imprecise probability : theories and applications July 2-5 2013, Compiègne, France (311-317)
• Troffaes, M. C., & Skulj, D. (2013). Model Checking for Imprecise Markov Chains. In F. Cozman, T. Denoeux, S. Destercke, & T. Seidenfeld (Eds.), ISIPTA ’13 : proceedings of the eighth international symposium on imprecise probability : theories and applications July 2-5 2013, Compiègne, France (337-344)
• Troffaes, M. C., & Goldstein, M. (2013). A Note on the Temporal Sure Preference Principle and the Updating of Lower Previsions. In F. Cozman, T. Denoeux, S. Destercke, & T. Seidenfeld (Eds.), ISIPTA ’13 : proceedings of the eighth international symposium on imprecise probability : theories and applications July 2-5 2013, Compiègne, France (319-328)
• Troffaes, M. C., Kelly, D. L., & Walter, G. (2012). Imprecise Dirichlet Model for Common-Cause Failure.
• Troffaes, M. C., & Destercke, S. (2012). A Nested Approach to Multivariate Modelling Using Lower Previsions.
• Troffaes, M. C., & Destercke, S. (2011). Probability boxes on totally preordered spaces for multivariate modelling. In F. Coolen, G. de Cooman, T. Fetz, & M. Oberguggenberger (Eds.),
• Troffaes, M. C., & Hable, R. (2011). Robustness of Natural Extension. In F. Coolen, G. de Cooman, T. Fetz, & M. Oberguggenberger (Eds.),
• Troffaes, M. C., & Gosling, J. P. (2011). Robust detection of exotic infectious diseases in animal herds: A comparative study of two decision methodologies under severe uncertainty. In F. Coolen, G. de Cooman, T. Fetz, & M. Oberguggenberger (Eds.),
• Huntley, N., & Troffaes, M. C. (2011). Dynamic Programming and Subtree Perfectness for Deterministic Discrete-Time Systems with Uncertain Rewards. In F. Coolen, G. de Cooman, T. Fetz, & M. Oberguggenberger (Eds.),
• Troffaes, M. C., Miranda, E., & Destercke, S. (2011). On the connection between probability boxes and possibility measures.
• Troffaes, M. C., Miranda, E., & Destercke, S. (2011). On P-Boxes and Random Sets.
• Troffaes, M. C., Kelly, D. L., & Walter, G. (2011). Elicitation and Inference for the Imprecise Dirichlet Model with Arbitrary Sets of Hyperparameters.
• Troffaes, M. C., Huntley, N., & Shirota Filho, R. (2010). Sequential Decision Processes under Act-State Independence with Arbitrary Choice Functions. In E. Hüllermeier, R. Kruse, & F. Hoffmann (Eds.),
• (2009). Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications. In T. Augustin, F. P. Coolen, S. Moral, & M. C. M. Troffaes (Eds.),
• Miranda, E., Troffaes, M., & Destercke, S. (2008). Generalised p-boxes on totally ordered spaces.
• Huntley, N., & Troffaes, M. (2008). An efficient normal form solution to decision trees with lower previsions.
• Troffaes, M. C., & Coolen, F. P. (2007). On the use of the imprecise Dirichlet model in fault trees.
• Troffaes, M. C. (2007). Finite approximations to coherent choice. In G. D. C. de Cooman, J. Vejnarova, & M. Zaffalon (Eds.),
• Troffaes, M. C. (2006). Conditional Lower Previsions for Unbounded Random Quantities. In J. Lawry, E. Miranda, A. Bugarin, S. Li, M. Ángeles Gil, P. Grzegorzewski, & O. Hryniewicz (Eds.),
• De Cooman, G., Troffaes, M. C., & Miranda, E. (2005). n-Monotone lower previsions and lower integrals. In F. G. Cozman, R. Nau, & T. Seidenfeld (Eds.),
• Troffaes, M. C. (2004). Efficient and Robust Global Amino Acid Sequence Alignment with Uncertain Evolutionary Distances.
• Troffaes, M. C. (2004). Adaptive control without prior by dynamic programming. In B. De Jager, & V. Verdult (Eds.),
• Troffaes, M. C. (2004). Learning and Optimal Control of Imprecise Markov Decision Processes by Dynamic Programming Using the Imprecise Dirichlet Model. In L. Miguel, M. A. Gil, P. Grzegorzewski, O. Hyrniewicz, & J. Lawry (Eds.),
• Troffaes, M. C., & De Cooman, G. (2003). Reliable Interval Estimates for Download Times.
• Troffaes, M. C. (2003). Natural selection in aggregation or how evolutionary game theory may help in aggregating imprecise expert opinions.
• Troffaes, M. C. (2003). Uncertainty and Conflict: A Behavioural Approach to the Aggregation of Expert Opinions. In J. Vejnarova (Ed.),
• De Cooman, G., & Troffaes, M. C. (2003). Dynamic Programming for Discrete-Time Systems with Uncertain Gain. In J. Bernard, T. Seidenfeld, & M. Zaffalon (Eds.),
• Troffaes, M. C., & De Cooman, G. (2002). The combination of conflicting information.
• Troffaes, M. C., & De Cooman, G. (2002). Lower Previsions for Unbounded Random Variables. In P. Grzegorzewski, L. OHryniewicz, & M. A. Gil (Eds.),
• Troffaes, M. C., & De Cooman, G. (2002). Extension of Coherent Lower Previsions to Unbounded Random Variables.
• De Cooman, G., & Troffaes, M. C. (2002). Optimal Control with Imprecise Gain through Dynamic Programming. In B. De Jager, & H. Zwart (Eds.),
• Troffaes, M. C., De Cooman, G., & Aeyels, D. (2001). Imprecise probabilities - discussion and open problems.
• Troffaes, M. C., De Cooman, G., & Aeyels, D. (2001). Optimal control under imprecision.

Doctoral Thesis

• Troffaes, M. C. (2005). Optimality, Uncertainty, and Dynamic Programming with Lower Previsions. (Thesis). Department of Electrical Energy, Systems & Automation, Universiteit Gent. Retrieved from https://durham-repository.worktribe.com/output/1618118

Edited book

• Augustin, T., Coolen, F. P., De Cooman, G., & Troffaes, M. C. (Eds.). (2014). Introduction to Imprecise Probabilities. Wiley
• Coolen-Schrijner, P., Coolen, F. P., Troffaes, M. C., Augustin, T., & Gupta, S. (Eds.). (2009). Imprecision in Statistical Theory and Practice. Grace Scientific Publishing

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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Sahlin, U., & Troffaes, M. C. (2021). A note on EFSA’s ongoing efforts to increase transparency of uncertainty in scientific opinions. Journal of Risk Research, 24(5), 545-552. https://doi.org/10.1080/13669877.2017.1313769
• 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
• Naghshbandi, S. N., Varga, L., Purvis, A., Mcwilliam, R., Minisci, E., Vasile, M., …Jones, D. H. (2020). A review of methods to study resilience of complex engineering and engineered systems. IEEE Access, 8(1), 87775-87799. https://doi.org/10.1109/access.2020.2992239
• Nakharutai, N., Troffaes, M. C., & Caiado, C. (2019). Improving and benchmarking of algorithms for decision making with lower previsions. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 113, 91-105. https://doi.org/10.1016/j.ijar.2019.06.008
• Nakharutai, N., Caiado, C. C., & Troffaes, M. C. (2019). Evaluating betting odds and free coupons using desirability. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 106, 128-145. https://doi.org/10.1016/j.ijar.2019.01.002
• Nakharutai, N., Troffaes, M. C., & Caiado, C. C. (2018). Improved linear programming methods for checking avoiding sure loss. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 101, 293-310. https://doi.org/10.1016/j.ijar.2018.07.013
• Troffaes, M. C. (2018). Imprecise Monte Carlo simulation and iterative importance sampling for the estimation of lower previsions. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 101, 31-48. https://doi.org/10.1016/j.ijar.2018.06.009
• Edwards, G., Sheehy, S., Dent, C., & Troffaes, M. C. (2017). Assessing the Contribution of Nightly Rechargeable Grid-Scale Storage to Generation Capacity Adequacy. Sustainable Energy, Grids and Networks, 12, 69-81. https://doi.org/10.1016/j.segan.2017.09.005
• Miranda, E., Troffaes, M. C., & Destercke, S. (2015). A geometric and game-theoretic study of the conjunction of possibility measures. Information Sciences, 298, 373-389. https://doi.org/10.1016/j.ins.2014.10.067
• Greenwood, D. M., Gentle, J. P., Myers, K. S., Davison, P. J., West, I. J., Bush, J. W., …Troffaes, M. C. (2014). A Comparison of Real Time Thermal Rating Systems in the U.S. and the UK. IEEE Transactions on Power Delivery, 29(4), 1849-1858. https://doi.org/10.1109/tpwrd.2014.2299068
• Troffaes, M. C., Walter, G., & Kelly, D. (2014). A robust Bayesian approach to modelling epistemic uncertainty in common-cause failure models. Reliability Engineering & System Safety, 125, 13-21. https://doi.org/10.1016/j.ress.2013.05.022
• Troffaes, M. C., Miranda, E., & Destercke, S. (2013). On the connection between probability boxes and possibility measures. Information Sciences, 224, 88-108. https://doi.org/10.1016/j.ins.2012.09.033
• Troffaes, M. C., & Gosling, J. P. (2012). Robust detection of exotic infectious diseases in animal herds: A comparative study of three decision methodologies under severe uncertainty. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 53(8), 1271-1281. https://doi.org/10.1016/j.ijar.2012.06.020
• Huntley, N., & Troffaes, M. C. (2012). Normal Form Backward Induction for Decision Trees with Coherent Lower Previsions. Annals of Operations Research, 195(1), 111-134. https://doi.org/10.1007/s10479-011-0968-2
• Troffaes, M. C., & Destercke, S. (2011). Probability boxes on totally preordered spaces for multivariate modelling. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 52(6), 767-791. https://doi.org/10.1016/j.ijar.2011.02.001
• Augustin, T., Coolen, F. P., Moral, S., & Troffaes, M. C. (2010). Imprecise probability in statistical inference and decision making. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 51(9), 1011-1013. https://doi.org/10.1016/j.ijar.2010.08.001
• Coolen, F. P., Oberguggenberger, M., & Troffaes, M. C. (2010). Uncertainty in Engineering Risk and Reliability: Introduction. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 224(4), i-vi
• Troffaes, M. (2009). Finite approximations to coherent choice. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 50(4), 655-665. https://doi.org/10.1016/j.ijar.2008.07.001
• Coolen-Schrijner, P., Coolen, F. P., Troffaes, M. C., & Augustin, T. (2009). Imprecision in Statistical Theory and Practice. Journal of statistical theory and practice, 3(1), 1-9
• Troffaes, M., & Coolen, F. (2009). Applying the imprecise Dirichlet model in cases with partial observations and dependencies in failure data. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 50(2), 257-268. https://doi.org/10.1016/j.ijar.2008.03.013
• de Cooman, G., Troffaes, M., & Miranda, E. (2008). n-Monotone exact functionals. Journal of Mathematical Analysis and Applications, 347(1), 143-156. https://doi.org/10.1016/j.jmaa.2008.05.071
• de Cooman, G., Troffaes, M., & Miranda, E. (2008). A unifying approach to integration for bounded positive charges. Journal of Mathematical Analysis and Applications, 340(2), 982-999. https://doi.org/10.1016/j.jmaa.2007.09.026
• Troffaes, M. C. (2007). Decision making under uncertainty using imprecise probabilities. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 45(1), 17-29. https://doi.org/10.1016/j.ijar.2006.06.001
• Troffaes, M. C. (2006). Generalizing The Conjunction Rule for Aggregating Conflicting Expert Opinions. International Journal of Intelligent Systems, 21(3), 361-380. https://doi.org/10.1002/int.20140
• De Cooman, G., Troffaes, M. C., & Miranda, E. (2005). n-Monotone lower previsions. Journal of Intelligent & Fuzzy Systems, 16(4), 253-263
• De Cooman, G., & Troffaes, M. C. (2005). Dynamic Programming for Deterministic Discrete-Time Systems with Uncertain Gain. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 39(2-3), 257-278. https://doi.org/10.1016/j.ijar.2004.10.004
• De Cooman, G., & Troffaes, M. C. (2004). Coherent lower previsions in systems modelling: products and aggregation rules. Reliability Engineering & System Safety, 85(1-3), 113-134. https://doi.org/10.1016/j.ress.2004.03.007

Masters Thesis

• Troffaes, M. C. (2000). Quantum algorithmes: theoretische aspecten en toepassingen. (Dissertation). Universiteit Gent. Retrieved from https://durham-repository.worktribe.com/output/1681064

Other (Print)

• Troffaes, M. C. (2004). Decision Making with Imprecise Probabilities: A Short Review

Presentation

• Troffaes, M. C. M., Kazemtabrizi, B., Smallbone, A., Bains, H., Jenkins, A., & McKeever, P. (2023, July). Using probability bounding to improve decision making for offshore wind planning in industry. Poster presented at 13th International Symposium on Imprecise Probability: Theories and Applications, Oviedo, Spain
• Huntley, N., & Troffaes, M. (2009, July). Characterizing factuality in normal form sequential decision making. Paper presented at Sixth International Symposium on Imprecise Probability: Theories and Applications, Durham, England
• Troffaes, M. C. M. (2023, July). A constructive theory for conditional lower previsions only using rational valued probability mass functions with finite support. Poster presented at 13th International Symposium on Imprecise Probability: Theories and Applications, Oviedo, Spain
• Basu, T., Einbeck, J., Troffaes, M. C., & Forbes, A. (2019, July). Robust uncertainty quantification for measurement problems with limited information. Paper presented at ISIPTA 2019, Ghent, Belgium
• Nakharutai, N., Troffaes, M. C., & Caiado, C. (2019, July). Improving and benchmarking of algorithms for decision making with lower previsions. Paper presented at ISIPTA 2019, Ghent, Belgium
• Bains, H., Kazemtabrizi, B., Madariaga, A., & Troffaes, M. C. (2019, July). Using interval dominance and Gamma-maximin for decision making in offshore power transmission. Paper presented at ISIPTA 2019, Ghent, Belgium
• Basu, T., Einbeck, J., & Troffaes, M. C. (2019, December). A sensitivity analysis of adaptive lasso. Paper presented at Innovations in Data and Statistical Sciences (INDSTATS 2019), Mumbai, India
• Troffaes, M. C., & Kelly, D. (2011, December). Common-cause failure in wind turbines: an initial analysis. Paper presented at 7th International Symposium on Imprecise Probability: Theories and Applications, Innsbruck