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Imprecise stochastic processes in discrete time: global models, imprecise Markov chains, and ergodic theorems.

, , и . Int. J. Approx. Reason., (2016)

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First Steps Towards an Imprecise Poisson Process., и . ISIPTA, том 103 из Proceedings of Machine Learning Research, стр. 175-184. PMLR, (2019)Imprecise stochastic processes in discrete time: global models, imprecise Markov chains, and ergodic theorems., , и . Int. J. Approx. Reason., (2016)On the (dis)similarities between stationary imprecise and non-stationary precise uncertainty models in algorithmic randomness., , и . Int. J. Approx. Reason., (2022)Extreme Lower Previsions and Minkowski Indecomposability., и . ECSQARU, том 7958 из Lecture Notes in Computer Science, стр. 157-168. Springer, (2013)Imprecise Markov Models for Scalable and Robust Performance Evaluation of Flexi-Grid Spectrum Allocation Policies., , , и . CoRR, (2018)Expected time averages in Markovian imprecise jump processes: a graph-theoretic characterisation of weak ergodicity., , и . ISIPTA, том 215 из Proceedings of Machine Learning Research, стр. 379-389. PMLR, (2023)A Remarkable Equivalence between Non-Stationary Precise and Stationary Imprecise Uncertainty Models in Computable Randomness., , и . ISIPTA, том 147 из Proceedings of Machine Learning Research, стр. 244-253. PMLR, (2021)Imprecise Continuous-Time Markov Chains: Efficient Computational Methods with Guaranteed Error Bounds., и . ISIPTA, том 62 из Proceedings of Machine Learning Research, стр. 145-156. PMLR, (2017)Efficient Computation of Updated Lower Expectations for Imprecise Continuous-Time Hidden Markov Chains., , и . ISIPTA, том 62 из Proceedings of Machine Learning Research, стр. 193-204. PMLR, (2017)The Smallest Probability Interval a Sequence Is Random for: A Study for Six Types of Randomness., , и . ECSQARU, том 12897 из Lecture Notes in Computer Science, стр. 442-454. Springer, (2021)