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Refinement and Difference for Probabilistic Automata

Benoît Delahaye ; Uli Fahrenberg ; Kim G. Larsen ; Axel Legay.

This paper studies a difference operator for stochastic systems whose specifications are represented by Abstract Probabilistic Automata (APAs). In the case refinement fails between two specifications, the target of this operator is to produce a specification APA that represents all witness PAs of&nbsp;[&hellip;]

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A Complete Quantitative Deduction System for the Bisimilarity Distance on Markov Chains

Giorgio Bacci ; Giovanni Bacci ; Kim G. Larsen ; Radu Mardare.

In this paper we propose a complete axiomatization of the bisimilarity distance of Desharnais et al. for the class of finite labelled Markov chains. Our axiomatization is given in the style of a quantitative extension of equational logic recently proposed by Mardare, Panangaden, and Plotkin (LICS&nbsp;[&hellip;]

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Computing Probabilistic Bisimilarity Distances for Probabilistic Automata

Giorgio Bacci ; Giovanni Bacci ; Kim G. Larsen ; Radu Mardare ; Qiyi Tang ; Franck van Breugel.

The probabilistic bisimilarity distance of Deng et al. has been proposed as a robust quantitative generalization of Segala and Lynch's probabilistic bisimilarity for probabilistic automata. In this paper, we present a characterization of the bisimilarity distance as the solution of a simple&nbsp;[&hellip;]

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