PRISM 4.0: Verification of Probabilistic Real-Time Systems

By Marta Z. Kwiatkowska, Gethin Norman, David Parker. International Conference on Computer Aided Verification, 2011

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Exploring the influence of big data on city transport operations: a Markovian approach (2017, methodology): “The steady state equation given above can be reformulated as 𝑄𝑇𝜋𝑇 = 0, and the well-known methods for the solution of systems of linear equations of the form 𝐴𝑥 = 𝑏 can be used; see (Stewart, 1991) and (Kwiatkowska et al., 2011).”
Trusted Machine Learning for Probabilistic Models (2016, methodology): “We consider a model M in PRISM corresponding to Figure 1 where p = q = 0, i.e., we don’t consider any Model Repair.”
A Storm is Coming: A Modern Probabilistic Model Checker (2017, background): “Like its main competitors Prism [1], MRMC [2], and iscasMC [3], Storm relies on numerical and symbolic computations.”
Probabilistic programming (2014, methodology): “PRISM supports various kinds of probabilistic models such as Discrete Time Markov Chains, Continuous Time Markov Chains and Markov Decision Processes.”
An overview of fault tree analysis and its application in model based dependability analysis (2017, methodology): “A method of transforming SAML models into the input language of probabilistic model checker PRISM (Kwiatkowska, Norman, & Parker, 2011) was represented in (Güdemann & Ortmeier, 2011).”