Code
01SC3817
Duration
01 March 2017 → 31 August 2021
Funding
Regional and community funding: Special Research Fund
Promotor
Fellow
Research disciplines
-
Natural sciences
- Applied mathematics in specific fields
- Artificial intelligence
-
Social sciences
- Cognitive science and intelligent systems
Keywords
stochastic processes
imprecise probabilities
Markov processes
robust probabilistic inference
game-theoretic probability
martingales
Project description
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 society’s increasing demand for features such as robustness and reliability.