The Experts below are selected from a list of 126717 Experts worldwide ranked by ideXlab platform
Sriram Sankaranarayanan - One of the best experts on this subject based on the ideXlab platform.
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TACAS - Uncertainty Propagation Using Probabilistic Affine Forms and Concentration of Measure Inequalities
Tools and Algorithms for the Construction and Analysis of Systems, 2016Co-Authors: Olivier Bouissou, Eric Goubault, Sylvie Putot, Aleksandar Chakarov, Sriram SankaranarayananAbstract:We consider the problem of reasoning about the probability of assertion violations in straight-line, nonlinear computations involving uncertain quantities modeled as random Variables. Such computations are quite common in many areas such as cyber-physical systems and numerical computation. Our approach extends probabilistic affine forms, an interval-based calculus for precisely tracking how the distribution of a given Program Variable depends on uncertain inputs modeled as noise symbols. We extend probabilistic affine forms using the precise tracking of dependencies between noise symbols combined with the expectations and higher order moments of the noise symbols. Next, we show how to prove bounds on the probabilities that Program Variables take on specific values by using concentration of measure inequalities. Thus, we enable a new approach to this problem that explicitly avoids subdividing the domain of inputs, as is commonly done in the related work. We illustrate the approach in this paper on a variety of challenging benchmark examples, and thus study its applicability to uncertainty propagation.
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uncertainty propagation using probabilistic affine forms and concentration of measure inequalities
Tools and Algorithms for Construction and Analysis of Systems, 2016Co-Authors: Olivier Bouissou, Eric Goubault, Sylvie Putot, Aleksandar Chakarov, Sriram SankaranarayananAbstract:We consider the problem of reasoning about the probability of assertion violations in straight-line, nonlinear computations involving uncertain quantities modeled as random Variables. Such computations are quite common in many areas such as cyber-physical systems and numerical computation. Our approach extends probabilistic affine forms, an interval-based calculus for precisely tracking how the distribution of a given Program Variable depends on uncertain inputs modeled as noise symbols. We extend probabilistic affine forms using the precise tracking of dependencies between noise symbols combined with the expectations and higher order moments of the noise symbols. Next, we show how to prove bounds on the probabilities that Program Variables take on specific values by using concentration of measure inequalities. Thus, we enable a new approach to this problem that explicitly avoids subdividing the domain of inputs, as is commonly done in the related work. We illustrate the approach in this paper on a variety of challenging benchmark examples, and thus study its applicability to uncertainty propagation.
Olivier Bouissou - One of the best experts on this subject based on the ideXlab platform.
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TACAS - Uncertainty Propagation Using Probabilistic Affine Forms and Concentration of Measure Inequalities
Tools and Algorithms for the Construction and Analysis of Systems, 2016Co-Authors: Olivier Bouissou, Eric Goubault, Sylvie Putot, Aleksandar Chakarov, Sriram SankaranarayananAbstract:We consider the problem of reasoning about the probability of assertion violations in straight-line, nonlinear computations involving uncertain quantities modeled as random Variables. Such computations are quite common in many areas such as cyber-physical systems and numerical computation. Our approach extends probabilistic affine forms, an interval-based calculus for precisely tracking how the distribution of a given Program Variable depends on uncertain inputs modeled as noise symbols. We extend probabilistic affine forms using the precise tracking of dependencies between noise symbols combined with the expectations and higher order moments of the noise symbols. Next, we show how to prove bounds on the probabilities that Program Variables take on specific values by using concentration of measure inequalities. Thus, we enable a new approach to this problem that explicitly avoids subdividing the domain of inputs, as is commonly done in the related work. We illustrate the approach in this paper on a variety of challenging benchmark examples, and thus study its applicability to uncertainty propagation.
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uncertainty propagation using probabilistic affine forms and concentration of measure inequalities
Tools and Algorithms for Construction and Analysis of Systems, 2016Co-Authors: Olivier Bouissou, Eric Goubault, Sylvie Putot, Aleksandar Chakarov, Sriram SankaranarayananAbstract:We consider the problem of reasoning about the probability of assertion violations in straight-line, nonlinear computations involving uncertain quantities modeled as random Variables. Such computations are quite common in many areas such as cyber-physical systems and numerical computation. Our approach extends probabilistic affine forms, an interval-based calculus for precisely tracking how the distribution of a given Program Variable depends on uncertain inputs modeled as noise symbols. We extend probabilistic affine forms using the precise tracking of dependencies between noise symbols combined with the expectations and higher order moments of the noise symbols. Next, we show how to prove bounds on the probabilities that Program Variables take on specific values by using concentration of measure inequalities. Thus, we enable a new approach to this problem that explicitly avoids subdividing the domain of inputs, as is commonly done in the related work. We illustrate the approach in this paper on a variety of challenging benchmark examples, and thus study its applicability to uncertainty propagation.
Eric Goubault - One of the best experts on this subject based on the ideXlab platform.
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TACAS - Uncertainty Propagation Using Probabilistic Affine Forms and Concentration of Measure Inequalities
Tools and Algorithms for the Construction and Analysis of Systems, 2016Co-Authors: Olivier Bouissou, Eric Goubault, Sylvie Putot, Aleksandar Chakarov, Sriram SankaranarayananAbstract:We consider the problem of reasoning about the probability of assertion violations in straight-line, nonlinear computations involving uncertain quantities modeled as random Variables. Such computations are quite common in many areas such as cyber-physical systems and numerical computation. Our approach extends probabilistic affine forms, an interval-based calculus for precisely tracking how the distribution of a given Program Variable depends on uncertain inputs modeled as noise symbols. We extend probabilistic affine forms using the precise tracking of dependencies between noise symbols combined with the expectations and higher order moments of the noise symbols. Next, we show how to prove bounds on the probabilities that Program Variables take on specific values by using concentration of measure inequalities. Thus, we enable a new approach to this problem that explicitly avoids subdividing the domain of inputs, as is commonly done in the related work. We illustrate the approach in this paper on a variety of challenging benchmark examples, and thus study its applicability to uncertainty propagation.
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uncertainty propagation using probabilistic affine forms and concentration of measure inequalities
Tools and Algorithms for Construction and Analysis of Systems, 2016Co-Authors: Olivier Bouissou, Eric Goubault, Sylvie Putot, Aleksandar Chakarov, Sriram SankaranarayananAbstract:We consider the problem of reasoning about the probability of assertion violations in straight-line, nonlinear computations involving uncertain quantities modeled as random Variables. Such computations are quite common in many areas such as cyber-physical systems and numerical computation. Our approach extends probabilistic affine forms, an interval-based calculus for precisely tracking how the distribution of a given Program Variable depends on uncertain inputs modeled as noise symbols. We extend probabilistic affine forms using the precise tracking of dependencies between noise symbols combined with the expectations and higher order moments of the noise symbols. Next, we show how to prove bounds on the probabilities that Program Variables take on specific values by using concentration of measure inequalities. Thus, we enable a new approach to this problem that explicitly avoids subdividing the domain of inputs, as is commonly done in the related work. We illustrate the approach in this paper on a variety of challenging benchmark examples, and thus study its applicability to uncertainty propagation.
Sylvie Putot - One of the best experts on this subject based on the ideXlab platform.
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TACAS - Uncertainty Propagation Using Probabilistic Affine Forms and Concentration of Measure Inequalities
Tools and Algorithms for the Construction and Analysis of Systems, 2016Co-Authors: Olivier Bouissou, Eric Goubault, Sylvie Putot, Aleksandar Chakarov, Sriram SankaranarayananAbstract:We consider the problem of reasoning about the probability of assertion violations in straight-line, nonlinear computations involving uncertain quantities modeled as random Variables. Such computations are quite common in many areas such as cyber-physical systems and numerical computation. Our approach extends probabilistic affine forms, an interval-based calculus for precisely tracking how the distribution of a given Program Variable depends on uncertain inputs modeled as noise symbols. We extend probabilistic affine forms using the precise tracking of dependencies between noise symbols combined with the expectations and higher order moments of the noise symbols. Next, we show how to prove bounds on the probabilities that Program Variables take on specific values by using concentration of measure inequalities. Thus, we enable a new approach to this problem that explicitly avoids subdividing the domain of inputs, as is commonly done in the related work. We illustrate the approach in this paper on a variety of challenging benchmark examples, and thus study its applicability to uncertainty propagation.
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uncertainty propagation using probabilistic affine forms and concentration of measure inequalities
Tools and Algorithms for Construction and Analysis of Systems, 2016Co-Authors: Olivier Bouissou, Eric Goubault, Sylvie Putot, Aleksandar Chakarov, Sriram SankaranarayananAbstract:We consider the problem of reasoning about the probability of assertion violations in straight-line, nonlinear computations involving uncertain quantities modeled as random Variables. Such computations are quite common in many areas such as cyber-physical systems and numerical computation. Our approach extends probabilistic affine forms, an interval-based calculus for precisely tracking how the distribution of a given Program Variable depends on uncertain inputs modeled as noise symbols. We extend probabilistic affine forms using the precise tracking of dependencies between noise symbols combined with the expectations and higher order moments of the noise symbols. Next, we show how to prove bounds on the probabilities that Program Variables take on specific values by using concentration of measure inequalities. Thus, we enable a new approach to this problem that explicitly avoids subdividing the domain of inputs, as is commonly done in the related work. We illustrate the approach in this paper on a variety of challenging benchmark examples, and thus study its applicability to uncertainty propagation.
Aleksandar Chakarov - One of the best experts on this subject based on the ideXlab platform.
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TACAS - Uncertainty Propagation Using Probabilistic Affine Forms and Concentration of Measure Inequalities
Tools and Algorithms for the Construction and Analysis of Systems, 2016Co-Authors: Olivier Bouissou, Eric Goubault, Sylvie Putot, Aleksandar Chakarov, Sriram SankaranarayananAbstract:We consider the problem of reasoning about the probability of assertion violations in straight-line, nonlinear computations involving uncertain quantities modeled as random Variables. Such computations are quite common in many areas such as cyber-physical systems and numerical computation. Our approach extends probabilistic affine forms, an interval-based calculus for precisely tracking how the distribution of a given Program Variable depends on uncertain inputs modeled as noise symbols. We extend probabilistic affine forms using the precise tracking of dependencies between noise symbols combined with the expectations and higher order moments of the noise symbols. Next, we show how to prove bounds on the probabilities that Program Variables take on specific values by using concentration of measure inequalities. Thus, we enable a new approach to this problem that explicitly avoids subdividing the domain of inputs, as is commonly done in the related work. We illustrate the approach in this paper on a variety of challenging benchmark examples, and thus study its applicability to uncertainty propagation.
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uncertainty propagation using probabilistic affine forms and concentration of measure inequalities
Tools and Algorithms for Construction and Analysis of Systems, 2016Co-Authors: Olivier Bouissou, Eric Goubault, Sylvie Putot, Aleksandar Chakarov, Sriram SankaranarayananAbstract:We consider the problem of reasoning about the probability of assertion violations in straight-line, nonlinear computations involving uncertain quantities modeled as random Variables. Such computations are quite common in many areas such as cyber-physical systems and numerical computation. Our approach extends probabilistic affine forms, an interval-based calculus for precisely tracking how the distribution of a given Program Variable depends on uncertain inputs modeled as noise symbols. We extend probabilistic affine forms using the precise tracking of dependencies between noise symbols combined with the expectations and higher order moments of the noise symbols. Next, we show how to prove bounds on the probabilities that Program Variables take on specific values by using concentration of measure inequalities. Thus, we enable a new approach to this problem that explicitly avoids subdividing the domain of inputs, as is commonly done in the related work. We illustrate the approach in this paper on a variety of challenging benchmark examples, and thus study its applicability to uncertainty propagation.