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Project

Imprecise continuous-time Markov chains

We aim to develop a theoretical framework, and efficient algorithms, for imprecise continuous-time Markov chains. The imprecision relates to the parameters of the model, which need not be specified exactly. This leads
to more robust and reliable results. The motivation stems from the popularity of traditional Markov chains, and from societyU+2019s increasing demand for features such as robustness and reliability.

Date:1 Mar 2017 →  31 Aug 2021
Keywords:martingales, Markov processes, imprecise probabilities, robust probabilistic inference, game-theoretic probability, stochastic processes
Disciplines:Applied mathematics in specific fields, Artificial intelligence, Cognitive science and intelligent systems