Towards a more robust treatment of discrete-time stochastic processes: developing a theoretical framework for working with imprecise probabilities in Markov chains

16 October 2017 → 15 October 2021
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Applied mathematics in specific fields
    • Statistics and numerical methods
    • Artificial intelligence
    • Computer architecture and networks
    • Distributed computing
    • Information sciences
    • Information systems
    • Programming languages
    • Scientific computing
    • Theoretical computer science
    • Visual computing
    • Other information and computing sciences
  • Social sciences
    • Cognitive science and intelligent systems
stochastic processes imprecise probabilities Markov chains robust probabilistic inference
Project description

We aim to develop a theoretical framework, and efficient algorithms, for imprecise discrete-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.