The Ins and Outs of the Probabilistic Model Checker MRMC

Katoen, Joost-Pieter; Zapreev, Ivan S.; Hahn, Ernst Moritz; Hermanns, Holger; Jansen, David N.

doi:10.1109/qest.2009.11

Cited by 110 publications

(80 citation statements)

References 32 publications

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“…The output of CADP is a .bcg file. This format is translated either into a .ctmdpi file, which is input to the Markov Reward Model Checker MRMC [15], or into an .ma file, which is the input of the Interactive Markov Chain Analyzer IMCA [13]. Finally, the requested dependability metrics are computed.…”

Section: Web Toolmentioning

confidence: 99%

“…The main problem in time-dependent reliability analysis is its complexity: The state space of models of real systems can grow arbitrarily large [15] and, thus, highly efficient techniques are required to yield results in a feasible time. Furthermore, an accurate modelling of all dependencies in these inherently complex systems requires an ever growing diversity of new gates.…”

Section: Introductionmentioning

confidence: 99%

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DFTCalc: A Tool for Efficient Fault Tree Analysis

Arnold

Belinfante

Berg

et al. 2013

Lecture Notes in Computer Science

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Abstract. Effective risk management is a key to ensure that our nuclear power plants, medical equipment, and power grids are dependable; and it is often required by law. Fault Tree Analysis (FTA) is a widely used methodology here, computing important dependability measures like system reliability. This paper presents DFTCalc, a powerful tool for FTA, providing (1) efficient fault tree modelling via compact representations; (2) effective analysis, allowing a wide range of dependability properties to be analysed (3) efficient analysis, via state-of-the-art stochastic techniques; and (4) a flexible and extensible framework, where gates can easily be changed or added. Technically, DFTCalc is realised via stochastic model checking, an innovative technique offering a wide plethora of powerful analysis techniques, including aggressive compression techniques to keep the underlying state space small.

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Section: Web Toolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DFTCalc: A Tool for Efficient Fault Tree Analysis

Arnold

Belinfante

Berg

et al. 2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…x c → k j=1 z c,i,j for all c ∈ Comm and 1 ≤ i ≤ |c| (11) where the current partition Π = π is given by π = {b 1 , . .…”

Section: Rewards a Markov Reward Model (Mrm) (D R)mentioning

confidence: 99%

“…In the last decade, probabilistic model checking has emerged as a viable and efficient alternative to classical analysis techniques for Markov chains, which typically focus on transient and long-run probabilities. This growing popularity is mainly due to the availability of ever improving software tools such as Prism [15] and Mrmc [11]. Like traditional model checkers, these tools suffer from the curse of dimensionality-the state space grows exponentially in the number of system components and variables.…”

Section: Introductionmentioning

confidence: 99%

SMT-Based Bisimulation Minimisation of Markov Models

Dehnert

Katoen

Parker

2013

Lecture Notes in Computer Science

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Abstract. Probabilistic model checking is an increasingly widely used formal verification technique. However, its dependence on computationally expensive numerical operations makes it particularly susceptible to the state-space explosion problem. Among other abstraction techniques, bisimulation minimisation has proven to shorten computation times significantly, but, usually, the full state space needs to be built prior to minimisation. We present a novel approach that leverages satisfiability solvers to extract the minimised system from a high-level description directly. A prototypical implementation in the framework of the probabilistic model checker Prism provides encouraging experimental results.

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“…The tool MARCIE [19] implements FAU but does not support cumulative rewards. Further tools that support reward properties but not FAU include, for instance, Möbius [4] and MRMC [11].…”

Section: Introductionmentioning

confidence: 99%

Computing Cumulative Rewards Using Fast Adaptive Uniformisation

Dannenberg¹,

Hahn²,

Kwiatkowska³

2013

Computational Methods in Systems Biology

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Abstract. The computation of transient probabilities for continuous-time Markov chains often employs uniformisation, also known as the Jensen's method. The fast adaptive uniformisation method introduced by Mateescu approximates the probability by neglecting insignificant states, and has proven to be effective for quantitative analysis of stochastic models arising in chemical and biological applications. However, this method has only been formulated for the analysis of properties at a given point of time t. In this paper, we extend fast adaptive uniformisation to handle expected reward properties which reason about the model behaviour until time t, for example, the expected number of chemical reactions that have occurred until t. To show the feasibility of the approach, we integrate the method into the probabilistic model checker PRISM and apply it to a range of biological models, demonstrating superior performance compared to existing techniques.

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The Ins and Outs of the Probabilistic Model Checker MRMC

Cited by 110 publications

References 32 publications

DFTCalc: A Tool for Efficient Fault Tree Analysis

DFTCalc: A Tool for Efficient Fault Tree Analysis

SMT-Based Bisimulation Minimisation of Markov Models

Computing Cumulative Rewards Using Fast Adaptive Uniformisation

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