Reliable Computing

, Volume 8, Issue 2, pp 97-113

First online:

Interval-Valued Finite Markov Chains

  • Igor O. KozineAffiliated withSystems Analysis Department, Risø National Laboratory
  • , Lev V. UtkinAffiliated withDepartment of Computer Science, Forest Technical Academy

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The requirement that precise state and transition probabilities be available is often not realistic because of cost, technical difficulties or the uniqueness of the situation under study. Expert judgements, generic data, heterogeneous and partial information on the occurrences of events may be sources of the probability assessments. All this source information cannot produce precise probabilities of interest without having to introduce drastic assumptions often of quite an arbitrary nature. in this paper the theory of interval-valued coherent previsions is employed to generalise discrete Markov chains to interval-valued probabilities. A general procedure of interval-valued probability elicitation is analysed as well. In addition, examples are provided.