178
T. Krak
17. G. De Cooman, J. De Bock, S. Lopatatzidis, Imprecise stochastic processes in discrete time:
global models, imprecise Markov chains, and ergodic theorems. Int. J. Approx. Reason. 76,
18–46 (2016)
18. S. Destercke, G. De Cooman, Relating epistemic irrelevance to event trees. Soft Methods for
Handling Variability and Imprecision (Springer, New York, 2008), pp. 66–73
19. A. Erreygers, J. De Bock, Imprecise continuous-time Markov chains: Efficient computational
methods with guaranteed error bounds, in Proceedings of ISIPTA 2017, pp. 145–156 (2017)
20. S. Galdino, Interval continuous-time Markov chains simulation, in Proceedings of the 2013
International Conference on Fuzzy Theory and Its Applications, pp. 273–278 (2013)
21. X. Guo, O. Hernández-Lerma, Continuous-time controlled Markov chains. Ann. Appl. Probab.
13(1), 363–388 (2003)
22. X. Guo, O. Hernández-Lerma, Continuous-Time Markov Decision Processes (Springer, New
York, 2009)
23. D.J. Hartfiel, Sequential limits in Markov set-chains. J. Appl. Probab. 28(4), 910–913 (1991)
24. D.J. Hartfiel, Markov Set-Chains. Lecture Notes in Mathematics, vol. 1695 (Springer, New
York, 1998)
25. D.J. Hartfiel, E. Seneta, On the theory of Markov set-chains. Adv. Appl. Probab. 26, 947–964
(1994)
26. F. Hermans, G. De Cooman, Characterisation of ergodic upper transition operators. Int. J.
Approx. Reason. 53(4), 573–583 (2012)
27. F. Hermans, D. Škulj, Stochastic processes, in Introduction to Imprecise Probabilities, ed. by
T. Augustin, F.P.A. Coolen, G. De Cooman, M.C.M. Troffaes (Wiley, New York, 2014)
28. H. Itoh, K. Nakamura, Partially observable Markov decision processes with imprecise parameters. Artificial Intelligence 171, 453–490 (2007)
29. I.O. Kozine, L.V. Utkin, Interval-valued finite Markov chains. Reliable Computing 8(2), 97–
113 (2002)
30. T. Krak, J. De Bock, A. Siebes, Imprecise continuous-time Markov chains. Int. J. Approx.
Reason. 88, 452–528 (2017)
31. T. Krak, J. De Bock, A. Siebes, Efficient computation of updated lower expectations for
imprecise continuous-time hidden Markov chains, in Proceedings of ISIPTA 2017, pp. 193–
204 (2017)
32. S. Lopatatzidis, Robust modelling and optimisation in stochastic processes using imprecise
probabilities, with an application to queueing theory. Ph.D. thesis, Ghent University (2017)
33. S. Lopatatzidis, J. De Bock, G. De Cooman, Calculating bounds on expected return and first
passage times in finite-state imprecise birth-death chains, in Proceedings of ISIPTA 2015, pp.
177–186 (2015)
34. S. Lopatatzidis, J. De Bock, G. De Cooman, Computational methods for imprecise continuoustime birth-death processes: a preliminary study of flipping times, in Proceedings of ISIPTA
2015, p. 344 (2015)
35. S. Lopatatzidis, J. De Bock, G. De Cooman, S. De Vuyst, J. Walraevens, Robust queueing
theory: an initial study using imprecise probabilities. Queueing Systems 82(1–2), 75–101
(2016)
36. S. Lopatatzidis, J. De Bock, G. De Cooman, Computing lower and upper expected first passage
and return times in imprecise birth-death chains. Int. J. Approx. Reason. 80, 137–173 (2017)
37. E. Miranda, G. De Cooman, Structural judgements, in Introduction to Imprecise Probabilities,
ed. by T. Augustin, F.P.A. Coolen, G. De Cooman, M.C.M. Troffaes (Wiley, New York, 2014)
38. J.R. Norris, Markov Chains (Cambridge University Press, Cambridge, 1998)
39. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
(Morgan Kaufmann, 1988)
40. C. Rottondi, A. Erreygers, G. Verticale, J. De Bock, Modelling spectrum assignment in a twoservice flexi-grid optical link with imprecise continuous-time Markov chains, in Proceedings
of DRCN 2017, pp. 39–46 (2017)
41. J.K. Satia, R.E. Lave, Markovian decision processes with uncertain transition probabilities.
Operations Research 21, 728–740 (1973)
T. Krak
17. G. De Cooman, J. De Bock, S. Lopatatzidis, Imprecise stochastic processes in discrete time:
global models, imprecise Markov chains, and ergodic theorems. Int. J. Approx. Reason. 76,
18–46 (2016)
18. S. Destercke, G. De Cooman, Relating epistemic irrelevance to event trees. Soft Methods for
Handling Variability and Imprecision (Springer, New York, 2008), pp. 66–73
19. A. Erreygers, J. De Bock, Imprecise continuous-time Markov chains: Efficient computational
methods with guaranteed error bounds, in Proceedings of ISIPTA 2017, pp. 145–156 (2017)
20. S. Galdino, Interval continuous-time Markov chains simulation, in Proceedings of the 2013
International Conference on Fuzzy Theory and Its Applications, pp. 273–278 (2013)
21. X. Guo, O. Hernández-Lerma, Continuous-time controlled Markov chains. Ann. Appl. Probab.
13(1), 363–388 (2003)
22. X. Guo, O. Hernández-Lerma, Continuous-Time Markov Decision Processes (Springer, New
York, 2009)
23. D.J. Hartfiel, Sequential limits in Markov set-chains. J. Appl. Probab. 28(4), 910–913 (1991)
24. D.J. Hartfiel, Markov Set-Chains. Lecture Notes in Mathematics, vol. 1695 (Springer, New
York, 1998)
25. D.J. Hartfiel, E. Seneta, On the theory of Markov set-chains. Adv. Appl. Probab. 26, 947–964
(1994)
26. F. Hermans, G. De Cooman, Characterisation of ergodic upper transition operators. Int. J.
Approx. Reason. 53(4), 573–583 (2012)
27. F. Hermans, D. Škulj, Stochastic processes, in Introduction to Imprecise Probabilities, ed. by
T. Augustin, F.P.A. Coolen, G. De Cooman, M.C.M. Troffaes (Wiley, New York, 2014)
28. H. Itoh, K. Nakamura, Partially observable Markov decision processes with imprecise parameters. Artificial Intelligence 171, 453–490 (2007)
29. I.O. Kozine, L.V. Utkin, Interval-valued finite Markov chains. Reliable Computing 8(2), 97–
113 (2002)
30. T. Krak, J. De Bock, A. Siebes, Imprecise continuous-time Markov chains. Int. J. Approx.
Reason. 88, 452–528 (2017)
31. T. Krak, J. De Bock, A. Siebes, Efficient computation of updated lower expectations for
imprecise continuous-time hidden Markov chains, in Proceedings of ISIPTA 2017, pp. 193–
204 (2017)
32. S. Lopatatzidis, Robust modelling and optimisation in stochastic processes using imprecise
probabilities, with an application to queueing theory. Ph.D. thesis, Ghent University (2017)
33. S. Lopatatzidis, J. De Bock, G. De Cooman, Calculating bounds on expected return and first
passage times in finite-state imprecise birth-death chains, in Proceedings of ISIPTA 2015, pp.
177–186 (2015)
34. S. Lopatatzidis, J. De Bock, G. De Cooman, Computational methods for imprecise continuoustime birth-death processes: a preliminary study of flipping times, in Proceedings of ISIPTA
2015, p. 344 (2015)
35. S. Lopatatzidis, J. De Bock, G. De Cooman, S. De Vuyst, J. Walraevens, Robust queueing
theory: an initial study using imprecise probabilities. Queueing Systems 82(1–2), 75–101
(2016)
36. S. Lopatatzidis, J. De Bock, G. De Cooman, Computing lower and upper expected first passage
and return times in imprecise birth-death chains. Int. J. Approx. Reason. 80, 137–173 (2017)
37. E. Miranda, G. De Cooman, Structural judgements, in Introduction to Imprecise Probabilities,
ed. by T. Augustin, F.P.A. Coolen, G. De Cooman, M.C.M. Troffaes (Wiley, New York, 2014)
38. J.R. Norris, Markov Chains (Cambridge University Press, Cambridge, 1998)
39. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
(Morgan Kaufmann, 1988)
40. C. Rottondi, A. Erreygers, G. Verticale, J. De Bock, Modelling spectrum assignment in a twoservice flexi-grid optical link with imprecise continuous-time Markov chains, in Proceedings
of DRCN 2017, pp. 39–46 (2017)
41. J.K. Satia, R.E. Lave, Markovian decision processes with uncertain transition probabilities.
Operations Research 21, 728–740 (1973)