Stochastic processes in general provide a popular framework for modelling uncertainty about the evolution of dynamical systems. The theory of Markov chains uses a number of crucial assumptions about the (in)dependence of such a process on its history that make their analysis tractable. In practice however, the parameters of a Markov chain may not be known exactly, or there may exist doubt as to the applicability of these assumptions to the system under study. This chapter presents an introduction to imprecise Markov chains, which are a robust generalisation of these models that may be used when parameters are not known exactly or when such assumptions could be violated. Their treatment is grounded in the theory of imprecise probabilities. The generalised model can be interpreted as a set of (traditional) stochastic processes, which may or may not be Markovian and which may have different and varying parameter values. Inferences are then performed to ensure robustness with respect to variations within this set. This chapter assumes no advanced familiarity with Markov chains or imprecise probability theory. It aims to develop an intuitive and graphical understanding of (imprecise) Markov chains in discrete and in continuous time.
|Title of host publication||Optimization Under Uncertainty with Applications to Aerospace Engineering|
|Number of pages||39|
|Publication status||Published - 15 Feb 2021|
- Imprecise Markov chains
- Imprecise probabilities
- Model uncertainty
- Stochastic processes