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Integrated Uncertainty Modelling, ed. by J. Lawry, E. Miranda, A. Bugarin, S. Li, M.A. Gil, P.
Grzegorzewski, O. Hryniewicz (Springer, New York, 2006), pp. 299–306
43. D. Škulj, Regular finite Markov chains with interval probabilities, in Proceedings of ISIPTA
2007, pp. 405–413 (2007)
44. D. Škulj, Efficient computation of the bounds of continuous time imprecise Markov chains.
Appl. Math. Comput. 250(C), 165–180 (2015)
45. D. Škulj, R. Hable, Coefficients of ergodicity for Markov chains with uncertain parameters.
Metrika 76(1), 107–133 (2013)
46. Y. Soullard, A. Antonucci, S. Destercke, Technical gestures recognition by set-valued hidden
Markov models with prior knowledge, in Soft Methods for Data Science, pp. 455–462 (2017)
47. M. Troffaes, J. Gledhill, D. Škulj, S. Blake, Using imprecise continuous time Markov chains
for assessing the reliability of power networks with common cause failure and non-immediate
repair, in Proceedings of ISIPTA 2015, pp. 287–294 (2015)
48. C.F. Van Loan, A Study of the Matrix Exponential, Numerical Analysis Report No. 10,
University of Manchester, Manchester, UK, August 1975, Reissued as MIMS EPrint 2006.397,
Manchester Institute for Mathematical Sciences, The University of Manchester, UK
49. V. Vovk, G. Shafer, Game-theoretic probability, in Introduction to Imprecise Probabilities, ed.
by T. Augustin, F.P.A. Coolen, G. De Cooman, M.C.M. Troffaes, (Wiley, New York, 2014)
50. P. Walley, Statistical Reasoning with Imprecise Probabilities (Chapman and Hall, London,
1991)
51. C.C. White, H.K. Eldeib, Markov decision-processes with imprecise transition-probabilities.
Operations Research 42, 739–749 (1994)
179
42. D. Škulj, Finite discrete time Markov chains with interval probabilities, in Soft Methods for
Integrated Uncertainty Modelling, ed. by J. Lawry, E. Miranda, A. Bugarin, S. Li, M.A. Gil, P.
Grzegorzewski, O. Hryniewicz (Springer, New York, 2006), pp. 299–306
43. D. Škulj, Regular finite Markov chains with interval probabilities, in Proceedings of ISIPTA
2007, pp. 405–413 (2007)
44. D. Škulj, Efficient computation of the bounds of continuous time imprecise Markov chains.
Appl. Math. Comput. 250(C), 165–180 (2015)
45. D. Škulj, R. Hable, Coefficients of ergodicity for Markov chains with uncertain parameters.
Metrika 76(1), 107–133 (2013)
46. Y. Soullard, A. Antonucci, S. Destercke, Technical gestures recognition by set-valued hidden
Markov models with prior knowledge, in Soft Methods for Data Science, pp. 455–462 (2017)
47. M. Troffaes, J. Gledhill, D. Škulj, S. Blake, Using imprecise continuous time Markov chains
for assessing the reliability of power networks with common cause failure and non-immediate
repair, in Proceedings of ISIPTA 2015, pp. 287–294 (2015)
48. C.F. Van Loan, A Study of the Matrix Exponential, Numerical Analysis Report No. 10,
University of Manchester, Manchester, UK, August 1975, Reissued as MIMS EPrint 2006.397,
Manchester Institute for Mathematical Sciences, The University of Manchester, UK
49. V. Vovk, G. Shafer, Game-theoretic probability, in Introduction to Imprecise Probabilities, ed.
by T. Augustin, F.P.A. Coolen, G. De Cooman, M.C.M. Troffaes, (Wiley, New York, 2014)
50. P. Walley, Statistical Reasoning with Imprecise Probabilities (Chapman and Hall, London,
1991)
51. C.C. White, H.K. Eldeib, Markov decision-processes with imprecise transition-probabilities.
Operations Research 42, 739–749 (1994)