IMPRECISE MARKOV CHAINS AND THEIR LIMIT BEHAVIOR

Abstract

When the initial and transition probabilities of a finite Markov chain in discrete time are not well known, we should perform a sensitivity analysis. This can be done by considering as basic uncertainty models the so-calledcredal setsthat these probabilities are known or believed to belong to and by allowing the probabilities to vary over such sets. This leads to the definition of animprecise Markov chain. We show that the time evolution of such a system can be studied very efficiently using so-calledlowerandupper expectations, which are equivalent mathematical representations of credal sets. We also study how the inferred credal set about the state at timenevolves asn→∞: under quite unrestrictive conditions, it converges to a uniquely invariant credal set, regardless of the credal set given for the initial state. This leads to a non-trivial generalization of the classical Perron–Frobenius theorem to imprecise Markov chains.