Results 1  10
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25
The ins and outs of the probabilistic model checker MRMC
 in Proc. QEST’09
, 2009
"... The Markov Reward Model Checker (MRMC) is a software tool for verifying properties over probabilistic models. It supports PCTL and CSL model checking, and their reward extensions. Distinguishing features of MRMC are its support for computing time and rewardbounded reachability probabilities, (prop ..."
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Cited by 29 (4 self)
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The Markov Reward Model Checker (MRMC) is a software tool for verifying properties over probabilistic models. It supports PCTL and CSL model checking, and their reward extensions. Distinguishing features of MRMC are its support for computing time and rewardbounded reachability probabilities, (propertydriven) bisimulation minimization, and precise onthefly steadystate detection. Recent tool features include timebounded reachability analysis for uniform CTMDPs and CSL model checking by discreteevent simulation. This paper presents the tool’s current status and its implementation details. 1.
A Bayesian Approach to Model Checking Biological Systems ⋆
"... Abstract. Recently, there has been considerable interest in the use of Model Checking for Systems Biology. Unfortunately, the state space of stochastic biological models is often too large for classical Model Checking techniques. For these models, a statistical approach to Model Checking has been sh ..."
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Cited by 18 (8 self)
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Abstract. Recently, there has been considerable interest in the use of Model Checking for Systems Biology. Unfortunately, the state space of stochastic biological models is often too large for classical Model Checking techniques. For these models, a statistical approach to Model Checking has been shown to be an effective alternative. Extending our earlier work, we present the first algorithm for performing statistical Model Checking using Bayesian Sequential Hypothesis Testing. We show that our Bayesian approach outperforms current statistical Model Checking techniques, which rely on tests from Classical (aka Frequentist) statistics, by requiring fewer system simulations. Another advantage of our approach is the ability to incorporate prior Biological knowledge about the model being verified. We demonstrate our algorithm on a variety of models from the Systems Biology literature and show that it enables faster verification than stateoftheart techniques, even when no prior knowledge is available. 1
Bayesian Statistical Model Checking with Application to Stateflow/Simulink Verification
, 2010
"... We address the problem of model checking stochastic systems, i.e. checking whether a stochastic system satisfies a certain temporal property with a probability greater (or smaller) than a fixed threshold. In particular, we present a novel Statistical Model Checking (SMC) approach based on Bayesian s ..."
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Cited by 15 (5 self)
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We address the problem of model checking stochastic systems, i.e. checking whether a stochastic system satisfies a certain temporal property with a probability greater (or smaller) than a fixed threshold. In particular, we present a novel Statistical Model Checking (SMC) approach based on Bayesian statistics. We show that our approach is feasible for hybrid systems with stochastic transitions, a generalization of Simulink/Stateflow models. Standard approaches to stochastic (discrete) systems require numerical solutions for large optimization problems and quickly become infeasible with larger state spaces. Generalizations of these techniques to hybrid systems with stochastic effects are even more challenging. The SMC approach was pioneered by Younes and Simmons in the discrete and nonBayesian case. It solves the verification problem by combining randomized sampling of system traces (which is very efficient for Simulink/Stateflow) with hypothesis testing or estimation. We believe SMC is essential for scaling up to large Stateflow/Simulink models. While the answer to the verification problem is not guaranteed to be correct, we prove that Bayesian SMC can make the probability of giving a wrong answer arbitrarily small. The advantage is that answers can usually be obtained much faster than with standard, exhaustive model checking
Quantitative Verification: Models, Techniques and Tools
, 2007
"... Automated verification is a technique for establishing if certain properties, usually expressed in temporal logic, hold for a system model. The model can be defined using a highlevel formalism or extracted directly from software using methods such as abstract interpretation. The verification procee ..."
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Cited by 14 (6 self)
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Automated verification is a technique for establishing if certain properties, usually expressed in temporal logic, hold for a system model. The model can be defined using a highlevel formalism or extracted directly from software using methods such as abstract interpretation. The verification proceeds through exhaustive exploration of the statetransition graph of the model and is therefore more powerful than testing. Quantitative verification is an analogous technique for establishing quantitative properties of a system model, such as the probability of battery power dropping below minimum, the expected time for message delivery and the expected number of messages lost before protocol termination. Models analysed through this method are typically variants of Markov chains, annotated with costs and rewards that describe resources and their usage during execution. Properties are expressed in temporal logic extended with probabilistic and reward operators. Quantitative verification involves a combination of a traversal of the statetransition graph of the model and numerical computation. This paper gives a brief overview of current research in quantitative verification, concentrating on the potential of the method and outlining future challenges. The modelling approach is described and the usefulness of the methodology illustrated with an example of a realworld protocol standard – Bluetooth device discovery – that has been analysed using the PRISM model checker (www.prismmodelchecker.org).
Formal modeling and analysis of DoS using probabilistic rewrite theories
 In International Workshop on Foundations of Computer Security (FCS’05) (Affiliated with LICS’05
, 2005
"... Existing models for analyzing the integrity and confidentiality of protocols need to be extended to enable the analysis of availability. Prior work on such extensions shows promising applications to the development of new DoS countermeasures. Ideally, it should be possible to apply these countermeas ..."
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Cited by 10 (5 self)
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Existing models for analyzing the integrity and confidentiality of protocols need to be extended to enable the analysis of availability. Prior work on such extensions shows promising applications to the development of new DoS countermeasures. Ideally, it should be possible to apply these countermeasures systematically in a way that preserves desirable properties already established. This paper investigates a step toward achieving this ideal by describing a way to expand term rewriting theories to include probabilistic aspects that can be used to show the effectiveness of DoS countermeasures. In particular, we consider the shared channel model, in which adversaries and valid participants share communication bandwidth according to a probabilistic interleaving model, and a countermeasure known as selective verification applied to the handshake steps of the TCP reliable transport protocol. These concepts are formulated in a probabilistic extension of the Maude term rewriting system, called PMAUDE. Furthermore, we formally verified the desired properties of the countermeasures through automatic statistical modelchecking techniques. 1
Extended directed search for probabilistic timed reachability
 In FORMATS’06, volume 4202 of LNCS
, 2006
"... Abstract. Current numerical model checkers for stochastic systems can efficiently analyse stochastic models. However, the fact that they are unable to provide debugging information constrains their practical use. In precursory work we proposed a method to select diagnostic traces, in the parlance of ..."
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Cited by 9 (5 self)
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Abstract. Current numerical model checkers for stochastic systems can efficiently analyse stochastic models. However, the fact that they are unable to provide debugging information constrains their practical use. In precursory work we proposed a method to select diagnostic traces, in the parlance of functional model checking commonly referred to as failure traces or counterexamples, for probabilistic timed reachability properties on discretetime and continuoustime Markov chains. We applied directed explicitstate search algorithms, like Z ∗ , to determine a diagnostic trace which carries large amount of probability. In this paper we extend this approach to determining sets of traces that carry large probability mass, since properties of stochastic systems are typically not violated by single traces, but by collections of those. To this end we extend existing heuristics guided search algorithms so that they select sets of traces. The result is provided in the form of a Markov chain. Such diagnostic Markov chains are not just essential tools for diagnostics and debugging but, they also allow the solution of timed reachability probability to be approximated from below. In particular cases, they also provide real counterexamples which can be used to show the violation of the given property. Our algorithms have been implemented in the stochastic model checker PRISM. We illustrate the applicability of our approach using a number of case studies. 1
How Fast and Fat Is Your Probabilistic Model Checker? an experimental performance comparison ⋆
"... Abstract. This paper studies the efficiency of several probabilistic model checkers by comparing verification times and peak memory usage for a set of standard case studies. The study considers the model checkers ETMCC, MRMC, PRISM (sparse and hybrid mode), YMER and VESTA, and focuses on fully proba ..."
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Cited by 7 (0 self)
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Abstract. This paper studies the efficiency of several probabilistic model checkers by comparing verification times and peak memory usage for a set of standard case studies. The study considers the model checkers ETMCC, MRMC, PRISM (sparse and hybrid mode), YMER and VESTA, and focuses on fully probabilistic systems. Several of our experiments show significantly different run times and memory consumptions between the tools—up to various orders of magnitude—without, however, indicating a clearly dominating tool. For statistical model checking YMER clearly prevails whereas for the numerical tools MRMC and PRISM (sparse) are rather close.
HASL: An expressive language for statistical verification of stochastic models
 IN: VALUETOOLS 2011
, 2011
"... We introduce the Hybrid Automata Stochastic Logic (HASL), a new temporal logic formalism for the verification of discrete event stochastic processes (DESP). HASL employs Linear Hybrid Automata (LHA) as machineries to select prefixes of relevant execution paths of a DESP D. The advantage with LHA is ..."
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Cited by 7 (6 self)
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We introduce the Hybrid Automata Stochastic Logic (HASL), a new temporal logic formalism for the verification of discrete event stochastic processes (DESP). HASL employs Linear Hybrid Automata (LHA) as machineries to select prefixes of relevant execution paths of a DESP D. The advantage with LHA is that rather elaborate information can be collected onthefly during path selection, providing the user with a powerful means to express sophisticated measures. A formula of HASL consists of an LHA A and an expression Z referring to moments of path random variables. A simulationbased statistical engine is employed to obtained a confidenceinterval estimate of the expected value of Z. In essence HASL provide a unifying verification framework where sophisticated temporal reasoning is naturally blended with elaborate rewardbased analysis. We illustrate the HASL approach by means of some examples and a discussion about its expressivity. We also provide empirical evidence obtained through COSMOS, a prototype software tool for HASL verification.
Statistical model checking: An overview
 RV 2010
, 2010
"... Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds. The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical a ..."
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Cited by 7 (1 self)
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Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds. The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical approach [31,8,35,22,21,5] that iteratively computes (or approximates) the exact measure of paths satisfying relevant subformulas; the algorithms themselves depend on the class of systems being analyzed as well as the logic used for specifying the properties. Another approach to solve the model checking problem is to simulate the system for finitely many executions, and use hypothesis testing to infer whether the samples provide a statistical evidence for the satisfaction or violation of the specification. In this tutorial, we survey the statistical approach, and outline its main advantages in terms of efficiency, uniformity, and simplicity.
Generalized Queries and Bayesian Statistical Model Checking in Dynamic Bayesian Networks: Application to Personalized Medicine
 In: Proc. 8th Ann. Intnl Conf. on Comput. Sys. Bioinf. (CSB
, 2009
"... We introduce the concept of generalized probabilistic queries in Dynamic Bayesian Networks (DBN) — computing P (φ1φ2), where φi is a formula in temporal logic encoding an equivalence class of trajectories through the variables of the model. Generalized queries include as special cases traditional ..."
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Cited by 5 (1 self)
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We introduce the concept of generalized probabilistic queries in Dynamic Bayesian Networks (DBN) — computing P (φ1φ2), where φi is a formula in temporal logic encoding an equivalence class of trajectories through the variables of the model. Generalized queries include as special cases traditional query types for DBNs (i.e., filtering, smoothing, prediction, and classification), but can also be used to express inference problems that are either impossible, or impractical to answer using traditional algorithms for inference in DBNs. We then discuss the relationship between answering generalized queries and the Probabilistic Model Checking Problem and introduce two novel algorithms for efficiently estimating P (φ1φ2) in a Bayesian fashion. Finally, we demonstrate our method by answering generalized queries that arise in the context of critical care medicine. Specifically, we show that our approach can be used to make treatment decisions for a cohort of 1,000 simulated sepsis patients, and that it outperforms Support Vector Machines, Neural Networks, and Random Forests on the same task.