Combinatorial Conversion and Moment Bisimulation for Stochastic Rewriting Systems

Autor: Behr, Nicolas, Danos, Vincent, Garnier, Ilias

Publikováno v: Logical Methods in Computer Science, Volume 16, Issue 3 (July 10, 2020) lmcs:5426

We develop a novel method to analyze the dynamics of stochastic rewriting systems evolving over finitary adhesive, extensive categories. Our formalism is based on the so-called rule algebra framework and exhibits an intimate relationship between the

Externí odkaz: http://arxiv.org/abs/1904.07313

The algebras of graph rewriting

Autor: Behr, Nicolas, Danos, Vincent, Garnier, Ilias, Heindel, Tobias

The concept of diagrammatic combinatorial Hopf algebras in the form introduced for describing the Heisenberg-Weyl algebra in~\cite{blasiak2010combinatorial} is extended to the case of so-called rule diagrams that present graph rewriting rules and the

Externí odkaz: http://arxiv.org/abs/1612.06240

On the reaction time of some synchronous systems

Autor: Garnier, Ilias, Aussaguès, Christophe, David, Vincent, Vidal-Naquet, Guy

Publikováno v: EPTCS 59, 2011, pp. 69-83

This paper presents an investigation of the notion of reaction time in some synchronous systems. A state-based description of such systems is given, and the reaction time of such systems under some classic composition primitives is studied. Reaction

Externí odkaz: http://arxiv.org/abs/1108.0467

Pointless learning (long version)

Autor: Clerc, Florence, Danos, Vincent, Dahlqvist, Fredrik, Garnier, Ilias

Publikováno v: Proceedings of FoSSaCS 2017
Proceedings of FoSSaCS 2017, 2017, Uppsala, Sweden
Accepted to the 20th International Conference on Foundations of Software Science and Computation .. 2017

Accepted to the 20th International Conference on Foundations of Software Science and Computation Structures (FoSSaCS) (pre-proceedings version); Bayesian inversion is at the heart of probabilistic programming and more generally machine learning. Unde

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::0cfcc2442cca9f52a8320a53089ab1f5
https://hal.archives-ouvertes.fr/hal-01974692

Computing continuous time Markov chains as transformers of unbounded observation functions

Autor: Danos, Vincent, Garnier, Ilias, Heindel, Tobias, Simonsen, Jakob Grue

Publikováno v: Danos, V, Garnier, I, Heindel, T & Simonsen, J G 2017, Computing continuous time Markov chains as transformers of unbounded observation functions . in Proceedings of the 20th International Conference on Foundations of Software Science and Computation Structures . Lecture Notes in Computer Science, vol. 10203, pp. 338-354, 20th International Conference on Foundations of Software Science and Computation Structures, Uppsala, Sweden, 22/04/17 . https://doi.org/10.1007/978-3-662-54458-7_20

This paper provides broad sufficient conditions for the computability of time-dependent averages of stochastic processes of the form f(Xt) where Xt is a continuous-time Markov chain (CTMC), and f is a real-valued function (aka an observable). We cons

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3094::9c908f8f7b9fcda14d012a6a8c13bdc4
https://hdl.handle.net/20.500.11820/2834688b-b524-49a0-99a6-65ac58e84d9f