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Semantics column

Semantics column SEM S EMANTICS C OLUMN MICHAEL MISLOVE, Tulane University mwm@math.tulane.edu This quarter ™s Semantics column continues a theme initiated by Prakash Panangaden in Issue 4 [5]. There Prakash described the history of probabilistic bisimulation, the fundamental relation characterizing when states of a probabilistic system are indistinguishable. Prakash focused his discussion around labelled Markov processes as models of probabilistic systems, and he described a logic he and his colleagues developed to characterize probabilistic bisimilarity in that setting. In this quarter ™s column, F RANCK VAN B REUGEL takes up this theme, this time providing a quantitative view, as opposed to the qualitative view that logic affords. Such an approach provides important insights “ with probabilistic processes, it ™s often not enough to know whether two states are bisimilar. Instead, one would like to reason about how œdissimilar  they are, and a distance can offer insight into this question. It turns out that one doesn ™t obtain a metric, but rather a pseudometric. To simplify the presentation, Franck uses a model in which the states are labelled. But as he points out, this approach is equivalent to the one Prakash used, in which the arrows relating states were labeled http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGLOG News Association for Computing Machinery

Semantics column

ACM SIGLOG News , Volume 4 (4) – Nov 3, 2017

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References (4)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
2372-3491
DOI
10.1145/3157831.3157836
Publisher site
See Article on Publisher Site

Abstract

SEM S EMANTICS C OLUMN MICHAEL MISLOVE, Tulane University mwm@math.tulane.edu This quarter ™s Semantics column continues a theme initiated by Prakash Panangaden in Issue 4 [5]. There Prakash described the history of probabilistic bisimulation, the fundamental relation characterizing when states of a probabilistic system are indistinguishable. Prakash focused his discussion around labelled Markov processes as models of probabilistic systems, and he described a logic he and his colleagues developed to characterize probabilistic bisimilarity in that setting. In this quarter ™s column, F RANCK VAN B REUGEL takes up this theme, this time providing a quantitative view, as opposed to the qualitative view that logic affords. Such an approach provides important insights “ with probabilistic processes, it ™s often not enough to know whether two states are bisimilar. Instead, one would like to reason about how œdissimilar  they are, and a distance can offer insight into this question. It turns out that one doesn ™t obtain a metric, but rather a pseudometric. To simplify the presentation, Franck uses a model in which the states are labelled. But as he points out, this approach is equivalent to the one Prakash used, in which the arrows relating states were labeled

Journal

ACM SIGLOG NewsAssociation for Computing Machinery

Published: Nov 3, 2017

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