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On-the-Fly Computation of Bisimilarity Distances

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

We propose a distance between continuous-time Markov chains (CTMCs) and study the problem of computing it by comparing three different algorithmic methodologies: iterative, linear program, and on-the-fly. In a work presented at FoSSaCS'12, Chen et al. characterized the bisimilarity distance 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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