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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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Sum and Tensor of Quantitative Effects

Giorgio Bacci ; Radu Mardare ; Prakash Panangaden ; Gordon Plotkin.

Inspired by the seminal work of Hyland, Plotkin, and Power on the combination of algebraic computational effects via sum and tensor, we develop an analogous theory for the combination of quantitative algebraic effects. Quantitative algebraic effects are monadic computational effects on categories of&nbsp;[&hellip;]

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