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David Parker - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Model Checking: Advances and Applications
Formal System Verification, 2017Co-Authors: Marta Kwiatkowska, Gethin Norman, David ParkerAbstract:Probabilistic Model checking is a powerful technique for formally verifying quantitative properties of systems that exhibit stochastic behaviour. Such systems are found in many application domains: for example, Probabilistic behaviour may arise due to the presence of failures in unreliable hardware, message loss in wireless communication channels, or the use of randomisation in distributed protocols. This chapter starts with an introduction to the technique of Probabilistic Model checking. We then survey some recent advances in the area, including controller synthesis, compositional verification, Probabilistic real-time systems and parametric Model checking. We illustrate the application of the various techniques with a combination of toy examples and descriptions of larger case studies. The chapter concludes with a discussion of some of the key challenges in the field.
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Advances in Probabilistic Model Checking
2012Co-Authors: Marta Kwiatkowska, David ParkerAbstract:Probabilistic Model checking is an automated verification method that aims to establish the correctness of Probabilistic systems. Probability may arise, for example, due to failures of unreliable components, communication across lossy media, or through the use of randomisation in distributed protocols. Probabilistic Model checking enables a range of exhaustive, quantitative analyses of properties such as "the probability of a message being delivered within 5ms is at least 0.89". In the last ten years, Probabilistic Model checking has been successfully applied to numerous real-world case studies, and is now a highly active field of research. This tutorial gives an introduction to Probabilistic Model checking, as well as presenting material on selected recent advances. The first half of the tutorial concerns two classical Probabilistic Models, discrete-time Markov chains and Markov decision processes, explaining the underlying theory and Model checking algorithms for the temporal logic PCTL. The second half discusses two advanced topics: quantitative abstraction refinement and Model checking for Probabilistic timed automata. We also briefly summarise the functionality of the Probabilistic Model checker PRISM, the leading tool in the area.
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Software Safety and Security - Advances in Probabilistic Model Checking
2012Co-Authors: Marta Kwiatkowska, David ParkerAbstract:Probabilistic Model checking is an automated verification method that aims to establish the correctness of Probabilistic systems. Probability may arise, for example, due to failures of unreliable components, communication across lossy media, or through the use of randomisation in distributed protocols. Probabilistic Model checking enables a range of exhaustive, quantitative analyses of properties such as "the probability of a message being delivered within 5ms is at least 0.89". In the last ten years, Probabilistic Model checking has been successfully applied to numerous real-world case studies, and is now a highly active field of research. This tutorial gives an introduction to Probabilistic Model checking, as well as presenting material on selected recent advances. The first half of the tutorial concerns two classical Probabilistic Models, discrete-time Markov chains and Markov decision processes, explaining the underlying theory and Model checking algorithms for the temporal logic PCTL. The second half discusses two advanced topics: quantitative abstraction refinement and Model checking for Probabilistic timed automata. We also briefly summarise the functionality of the Probabilistic Model checker PRISM, the leading tool in the area.
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Probabilistic Model checking for systems biology
2011Co-Authors: Marta Kwiatkowska, Gethin Norman, David ParkerAbstract:Probabilistic Model checking is a technique for formally verifying quantitative properties of systems that exhibit stochastic behaviour. In this chapter, we show how this approach can be applied to the study of biological systems such as biochemical reaction networks and signalling pathways. We present an introduction to the state-of-the-art Probabilistic Model checking tool PRISM using a case study based on the Fibroblast Growth Factor (FGF) signalling pathway.
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Advances and challenges of Probabilistic Model checking
2010 48th Annual Allerton Conference on Communication Control and Computing (Allerton), 2010Co-Authors: Marta Kwiatkowska, Gethin Norman, David ParkerAbstract:Probabilistic Model checking is a powerful technique for formally verifying quantitative properties of systems that exhibit stochastic behaviour. Such systems are found in many domains: Probabilistic behaviour may arise, for example, due to failures of unreliable components, communication across lossy media, or through the use of randomisation in distributed protocols. In this paper, we give a short overview of Probabilistic Model checking and of PRISM (www.prismModelchecker.org), currently the leading software tool in this area. We then mention some of the limitations of these techniques, describe some of the advances that are being made to overcome them, and outline key challenges that remain in this research area.
Marta Kwiatkowska - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Model Checking: Advances and Applications
Formal System Verification, 2017Co-Authors: Marta Kwiatkowska, Gethin Norman, David ParkerAbstract:Probabilistic Model checking is a powerful technique for formally verifying quantitative properties of systems that exhibit stochastic behaviour. Such systems are found in many application domains: for example, Probabilistic behaviour may arise due to the presence of failures in unreliable hardware, message loss in wireless communication channels, or the use of randomisation in distributed protocols. This chapter starts with an introduction to the technique of Probabilistic Model checking. We then survey some recent advances in the area, including controller synthesis, compositional verification, Probabilistic real-time systems and parametric Model checking. We illustrate the application of the various techniques with a combination of toy examples and descriptions of larger case studies. The chapter concludes with a discussion of some of the key challenges in the field.
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Software Systems Safety - Probabilistic Model Checking for Biology.
2014Co-Authors: Marta Kwiatkowska, Chris ThachukAbstract:Probabilistic Model checking is an automated method for verifying the correctness and performance of Probabilistic Models. Property specifications are expressed in Probabilistic temporal logic, denoting, for example, the probability of a given event, the probability of its occurrence within a given time interval, or expected number of times it has occurred in a time period. This chapter focuses on the application of Probabilistic Model checking to biological systems Modelled as continuous-time Markov chains, illustrating the usefulness of these techniques through relevant case studies performed with the Probabilistic Model checker PRISM. We begin with an introduction to discrete-time Markov chains and the corresponding Model checking algorithms. Then continuous-time Markov chain Models are defined, together with the logic CSL (Continuous Stochastic Logic), and an overview of Model checking for CSL is given, which proceeds mainly by reduction to discrete-time Markov chains. The techniques are illustrated with examples of biochemical reaction networks, which are verified against quantitative temporal properties. Next a biological case study analysing the Fibroblast Growth Factor (FGF) molecular signalling pathway is summarised, highlighting how Probabilistic Model checking can assist in scientific discovery. Finally, we consider DNA computation, and specifically the DSD formalism (DNA Strand Displacement), and show how errors can be detected in DNA gate designs, analogous to Model checking for digital circuits.
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Advances in Probabilistic Model Checking
2012Co-Authors: Marta Kwiatkowska, David ParkerAbstract:Probabilistic Model checking is an automated verification method that aims to establish the correctness of Probabilistic systems. Probability may arise, for example, due to failures of unreliable components, communication across lossy media, or through the use of randomisation in distributed protocols. Probabilistic Model checking enables a range of exhaustive, quantitative analyses of properties such as "the probability of a message being delivered within 5ms is at least 0.89". In the last ten years, Probabilistic Model checking has been successfully applied to numerous real-world case studies, and is now a highly active field of research. This tutorial gives an introduction to Probabilistic Model checking, as well as presenting material on selected recent advances. The first half of the tutorial concerns two classical Probabilistic Models, discrete-time Markov chains and Markov decision processes, explaining the underlying theory and Model checking algorithms for the temporal logic PCTL. The second half discusses two advanced topics: quantitative abstraction refinement and Model checking for Probabilistic timed automata. We also briefly summarise the functionality of the Probabilistic Model checker PRISM, the leading tool in the area.
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Software Safety and Security - Advances in Probabilistic Model Checking
2012Co-Authors: Marta Kwiatkowska, David ParkerAbstract:Probabilistic Model checking is an automated verification method that aims to establish the correctness of Probabilistic systems. Probability may arise, for example, due to failures of unreliable components, communication across lossy media, or through the use of randomisation in distributed protocols. Probabilistic Model checking enables a range of exhaustive, quantitative analyses of properties such as "the probability of a message being delivered within 5ms is at least 0.89". In the last ten years, Probabilistic Model checking has been successfully applied to numerous real-world case studies, and is now a highly active field of research. This tutorial gives an introduction to Probabilistic Model checking, as well as presenting material on selected recent advances. The first half of the tutorial concerns two classical Probabilistic Models, discrete-time Markov chains and Markov decision processes, explaining the underlying theory and Model checking algorithms for the temporal logic PCTL. The second half discusses two advanced topics: quantitative abstraction refinement and Model checking for Probabilistic timed automata. We also briefly summarise the functionality of the Probabilistic Model checker PRISM, the leading tool in the area.
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Probabilistic Model checking for systems biology
2011Co-Authors: Marta Kwiatkowska, Gethin Norman, David ParkerAbstract:Probabilistic Model checking is a technique for formally verifying quantitative properties of systems that exhibit stochastic behaviour. In this chapter, we show how this approach can be applied to the study of biological systems such as biochemical reaction networks and signalling pathways. We present an introduction to the state-of-the-art Probabilistic Model checking tool PRISM using a case study based on the Fibroblast Growth Factor (FGF) signalling pathway.
Gethin Norman - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Model Checking: Advances and Applications
Formal System Verification, 2017Co-Authors: Marta Kwiatkowska, Gethin Norman, David ParkerAbstract:Probabilistic Model checking is a powerful technique for formally verifying quantitative properties of systems that exhibit stochastic behaviour. Such systems are found in many application domains: for example, Probabilistic behaviour may arise due to the presence of failures in unreliable hardware, message loss in wireless communication channels, or the use of randomisation in distributed protocols. This chapter starts with an introduction to the technique of Probabilistic Model checking. We then survey some recent advances in the area, including controller synthesis, compositional verification, Probabilistic real-time systems and parametric Model checking. We illustrate the application of the various techniques with a combination of toy examples and descriptions of larger case studies. The chapter concludes with a discussion of some of the key challenges in the field.
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Probabilistic Model checking for systems biology
2011Co-Authors: Marta Kwiatkowska, Gethin Norman, David ParkerAbstract:Probabilistic Model checking is a technique for formally verifying quantitative properties of systems that exhibit stochastic behaviour. In this chapter, we show how this approach can be applied to the study of biological systems such as biochemical reaction networks and signalling pathways. We present an introduction to the state-of-the-art Probabilistic Model checking tool PRISM using a case study based on the Fibroblast Growth Factor (FGF) signalling pathway.
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Advances and challenges of Probabilistic Model checking
2010 48th Annual Allerton Conference on Communication Control and Computing (Allerton), 2010Co-Authors: Marta Kwiatkowska, Gethin Norman, David ParkerAbstract:Probabilistic Model checking is a powerful technique for formally verifying quantitative properties of systems that exhibit stochastic behaviour. Such systems are found in many domains: Probabilistic behaviour may arise, for example, due to failures of unreliable components, communication across lossy media, or through the use of randomisation in distributed protocols. In this paper, we give a short overview of Probabilistic Model checking and of PRISM (www.prismModelchecker.org), currently the leading software tool in this area. We then mention some of the limitations of these techniques, describe some of the advances that are being made to overcome them, and outline key challenges that remain in this research area.
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prism Probabilistic Model checking for performance and reliability analysis
Measurement and Modeling of Computer Systems, 2009Co-Authors: Marta Kwiatkowska, Gethin Norman, David ParkerAbstract:Probabilistic Model checking is a formal verification technique for the Modelling and analysis of stochastic systems. It has proved to be useful for studying a wide range of quantitative properties of Models taken from many diffierent application domains. This includes, for example, performance and reliability properties of computer and communication systems. In this paper, we give an overview of the Probabilistic Model checking tool PRISM, focusing in particular on its support for continuous-time Markov chains and Markov reward Models, and how these can be used to analyse performability properties.
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Probabilistic Model checking of complex biological pathways
Computational Methods in Systems Biology, 2008Co-Authors: John K Heath, Marta Kwiatkowska, David Parker, Gethin Norman, Oksana TymchyshynAbstract:Probabilistic Model checking is a formal verification technique that has been successfully applied to the analysis of systems from a broad range of domains, including security and communication protocols, distributed algorithms and power management. In this paper we illustrate its applicability to a complex biological system: the FGF (Fibroblast Growth Factor) signalling pathway. We give a detailed description of how this case study can be Modelled in the Probabilistic Model checker PRISM, discussing some of the issues that arise in doing so, and show how we can thus examine a rich selection of quantitative properties of this Model. We present experimental results for the case study under several different scenarios and provide a detailed analysis, illustrating how this approach can be used to yield a better understanding of the dynamics of the pathway. Finally, we outline a number of exact and approximate techniques to enable the verification of larger and more complex pathways and apply several of them to the FGF case study.
Hsiaochuan Wang - One of the best experts on this subject based on the ideXlab platform.
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a segmental Probabilistic Model of speech using an orthogonal polynomial representation application to text independent speaker verification
Speech Communication, 1996Co-Authors: Hsiaochuan WangAbstract:Abstract A segmental Probabilistic Model based on an orthogonal polynomial representation of speech signals is proposed. Unlike the conventional frame based Probabilistic Model, this segment based Model concatenates the similar acoustic characteristics of consecutive frames into an acoustic segment and represents the segment by an orthogonal polynomial function. An iterative algorithm that performs recognition and segmentation processes is proposed for estimating the segment Model. This segment Model is applied in the text independent speaker verification. Tests were carried out on a 20-speaker database. With the best version of the Model, an equal error rate of 0.59% can be reached, for test utterances of 10 digits. This corresponds to a relative error rate reduction of more than 50%, compared to the conventional frame based Probabilistic Model.
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an orthogonal polynomial representation of speech signals and its Probabilistic Model for text independent speaker verification
International Conference on Acoustics Speech and Signal Processing, 1995Co-Authors: Hsiaochuan Wang, F K Soong, Chaoshih HuangAbstract:A segmental Probabilistic Model based on an orthogonal polynomial representation of speech signals is proposed. Unlike the conventional frame based Probabilistic Model, this segment based Model concatenates the similar acoustic characteristics of consecutive frames into an acoustic segment and represents the segment by an orthogonal polynomial function. An algorithm which iteratively performs recognition and segmentation processes is proposed for estimating the parameters of the segment Model. This segment Model is applied in the text independent speaker verification. For a 20-speaker database, the experimental results show that the performance by using segment Models is better than that by using the conventional frame based Probabilistic Model. The equal error rate can be reduced by 3.6% when the Models are represented by 64-mixture density functions.
Christian Soize - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Model identification of the bit rock interaction Model uncertainties in nonlinear dynamics of a drill string
Mechanics Research Communications, 2010Co-Authors: Christian Soize, T.g. Ritto, R. SampaioAbstract:Abstract This paper deals with a procedure to perform the identification of the Probabilistic Model of uncertainties in a bit–rock interaction Model for the nonlinear dynamics of a drill-string. The bit–rock interaction Model is represented by a nonlinear constitutive equation, whose uncertainties are Modeled using the nonparametric Probabilistic approach. The identification of the parameter of this Probabilistic Model is carried out using the maximum likelihood method together with a statistical reduction in the frequency domain using the Principal Component Analysis.
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Probabilistic Model identification of uncertainties for the bit-rock interaction Model (local nonlinearity) of a drill-string system
2010Co-Authors: T.g. Ritto, Christian Soize, R. SampaioAbstract:This paper deals with a procedure to perform the identification of the Probabilistic Model of uncertainties for the bit-rock interaction Model of a drill-string system. The bit-rock interaction Model is represented by a nonlinear constitutive equation, whose uncertainties are Modeled using the nonparametric Probabilistic approach. The identification of the parameter of this Probabilistic Model is carried out using the Maximum Likelihood method together with a statistical reduction in the frequency domain using the Principal Component Analysis. This is the first time that a procedure is proposed to identify the Probabilistic Model, in the context of drill-string dynamics.
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Probabilistic Model identification of the bit-rock-interaction-Model uncertainties in nonlinear dynamics of a drill-string
Mechanics Research Communications, 2010Co-Authors: T.g. Ritto, Christian Soize, R. SampaioAbstract:This paper deals with a procedure to perform the identification of the Probabilistic Model of uncertainties in a bit-rock interaction Model for the nonlinear dynamics of a drill-string. The bit-rock interaction Model is represented by a nonlinear constitutive equation, whose uncertainties are Modeled using the nonparametric Probabilistic approach. The identification of the parameter of this Probabilistic Model is carried out using the maximum likelihood method together with a statistical reduction in the frequency domain using the Principal Component Analysis. (C) 2010 Elsevier Ltd. All rights reserved.
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Construction of a Probabilistic Model for impedance matrices
Computer Methods in Applied Mechanics and Engineering, 2007Co-Authors: Régis Cottereau, Didier Clouteau, Christian SoizeAbstract:Impedance matrices allow for the coupling of domains with very different properties, and possibly Modeled with different methods. This paper presents the construction of a Probabilistic Model for such matrices, using a nonparametric method that allows for the consideration of data errors as well as Model errors. To enable the application of this method in the case of a domain for which no Finite Element Model is available, the identification of a "hidden state variables Model" - from the knowledge of the impedance matrix at a discrete set of frequencies - is also described. Finally the construction of the Probabilistic Model of the impedance of a pile foundation on a layered unbounded soil illustrates the capabilities of the method.
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Construction of a Probabilistic Model for impedance matrices
Computer Methods in Applied Mechanics and Engineering, 2006Co-Authors: Régis Cottereau, Didier Clouteau, Christian SoizeAbstract:Impedance matrices allow for the coupling of domains with very different properties, and possibly Modeled with different methods. This paper presents the construction of a Probabilistic Model for such matrices, using a nonparametric method that allows for the consideration of data errors as well as Model errors. To enable the application of this method in the case of a domain for which no Finite Element Model is available, the identification of a "hidden state variables Model" - from the knowledge of the impedance matrix at a discrete set of frequencies - is also described. Finally the construction of the Probabilistic Model of the impedance of a pile foundation on a layered unbounded soil illustrates the capabilities of the method.
