I am a PhD student at Ghent University, more precisely at the Foundations Lab for Imprecise Probabilities (FLip) of the Department of Electronics and Information Systems, part of the Faculty of Engineering and Architecture.
Before October 2017, I was a member of the SMACS Research Group, part of the Department of Telecommunications and Information Processing, for two years.
From November 2019 until January 2020, I was on a three month long research stay at the UNIMODE research group of the University of Oviedo, Spain.
My research is focussed around the theory and applications of imprecise probabilities.
I first became acquainted with imprecise probabilities during my Master thesis, in which I used imprecise probabilities to model the noise in a one-dimensional discrete-time linear-quadratic optimal control problem.
On the theoretical side, I am currently working on stochastic processes, mainly (an imprecise extension of) the Poisson process and (ergodicity of) imprecise continuous-time Markov chains.
These stochastic process are used as models in a lot of fields.
From a practical point of view, my main interest lies in using these imprecise stochastic processes to robustly model queueing systems.
If interested, you can scroll down to take a look at my scientific output, brief academic CV or contact details.
Scientific output
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Alexander Erreygers & Jasper De Bock.
Bounding inferences for large-scale continuous-time Markov chains: A new approach based on lumping and imprecise Markov chains.
In International Journal of Approximate Reasoning,
115:96–133,
Dec. 2019.
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Alexander Erreygers, Jasper De Bock, Gert de Cooman & Arthur Van Camp.
Optimal control of a linear system subject to partially specified input noise.
In International Journal of Robust and Nonlinear Control,
29(12):3892–3914,
Aug. 2019.
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Alexander Erreygers, Cristina Rottondi, Giacomo Verticale & Jasper De Bock.
Imprecise Markov Models for Scalable and Robust Performance Evaluation of Flexi-Grid Spectrum Allocation Policies.
In IEEE Transactions on Communications,
66(11):5401–5414,
Nov. 2018.
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Alexander Erreygers & Enrique Miranda.
A study of the set of probability measures compatible with comparative judgements.
In the proceedings of the
18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2020),
Jun. 2020.
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Alexander Erreygers & Jasper De Bock.
First steps towards an imprecise Poisson process.
In the proceedings of the
11th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA 2019),
Jun. 2019.
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Thomas Krak, Alexander Erreygers & Jasper De Bock.
An Imprecise Probabilistic Estimator for the Transition Rate Matrix of a Continuous-Time Markov Chain.
In the proceedings of the
9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018),
Sep. 2018.
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Alexander Erreygers & Jasper De Bock.
Computing Inferences for Large-Scale Continuous-Time Markov Chains by Combining Lumping with Imprecision.
In the proceedings of the
9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018),
Sep. 2018.
Winner of the International Journal of Approximate Reasoning Best Paper Award.
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Alexander Erreygers & Jasper De Bock.
Imprecise continuous-time Markov chains: Efficient Computational methods with guaranteed error bounds.
In the proceedings of the
10th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA'17),
Jul. 2017.
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Cristina Rottondi, Alexander Erreygers, Giacomo Verticale & Jasper De Bock.
Modelling spectrum assignment in a two-service flexi-grid optical link with imprecise continuous-time Markov chains.
In the proceedings of the
13th International Conference on the Design of Reliable Communication Networks (DRCN 2017),
Mar. 2017.
Winner of the best paper award.
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Alexander Erreygers.
Extending the domain of Markovian imprecise jump processes.
Presented at the
International Conference on Uncertainty Quantification & Optimisation (UQOP 2020),
17/11/2020.
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Alexander Erreygers.
Imprecise stochastic processes.
Invited talk at the
University of Oviedo,
11/11/2019.
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Alexander Erreygers & Jasper De Bock.
Computing limit expectations of imprecise continuous-time Markov chains.
Presented at the
11th Workshop on Principles and Methods of Statistical Inference with Interval Probability (WPMSIIP 2018),
1/8/2018.
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Alexander Erreygers & Jasper De Bock.
Lumping continuous-time Markov chains.
Presented at the
10th Workshop on Principles and Methods of Statistical Inference with Interval Probability (WUML/WPMSIIP 2017),
12/9/2017.
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Alexander Erreygers & Jasper De Bock.
Towards an imprecise Poisson process.
Presented at the
10th Workshop on Principles and Methods of Statistical Inference with Interval Probability (WUML/WPMSIIP 2017),
12/9/2017.
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Alexander Erreygers & Jasper De Bock.
Handling the state space explosion of Markov chains: How lumping introduces imprecision (almost) inevitably.
Presented at the
10th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA'17),
14/7/2017.
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Cristina Rottondi, Alexander Erreygers, Giacomo Verticale & Jasper De Bock.
Modelling spectrum assignment in a two-service flexi-grid optical link with imprecise continuous-time Markov chains.
Presented at the
10th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA'17),
10/7/2017.
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Alexander Erreygers, Jasper De Bock, Gert de Cooman & Arthur Van Camp.
LQ optimal control for partially specified input noise.
Presented at the
28th European Conference on Operational Research (EURO2016),
6/7/2016.
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Alexander Erreygers, Jasper De Bock, Gert de Cooman & Arthur Van Camp.
Optimal control of linear systems with quadratic cost and imprecise forward irrelevant input noise.
Presented at the
9th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA'15),
22/7/2015.
Academic CV
Working experience
Aug. 2014 – Sep. 2014
During a 4 week internship I built a model of the KPI of a chemical plant using data mining techniques available in the JMP software package.
Teaching
2017-2018 – present
The aim of this course is to teach the student the basic principles and the reasoning methods of probability. Lecturer: Gert de Cooman