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Stochastic Model Checking with Stochastic Comparison

Authors: Nihal Pekergin; Sana Younès;

Stochastic Model Checking with Stochastic Comparison

Abstract

This paper presents a stochastic comparison based method to check state formulas defined over Discrete Time Markov Reward Models. High-level specifications like stochastic Petri nets, Stochastic Automata Networks, Stochastic Process Algebras have been developed to construct large Markov models. However computation of transient and steady-state distributions are limited to relatively small parameter sizes because of the state space explosion problem. Stochastic comparison technique by which both transient and steady-state bounding distributions can be computed, lets to overcome this problem. On the other hand, bounding techniques are useful in Model Checking, since we check generally formulas to see if they meet some bounds or not. We propose to apply stochastic bounding algorithms to construct bounding distributions and to check formulas through these distributions.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Top 10%
Average
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