%0 Conference Proceedings
%T Uncertainty propagation using probabilistic affine forms and concentration of measure inequalities
%+ Département Ingénierie Logiciels et Systèmes (DILS)
%+ Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX)
%+ University of Colorado [Boulder]
%A Bouissou, O.
%A Goubault, E.
%A Putot, S.
%A Chakarov, A.
%A Sankaranarayanan, S.
%Z Conference of 22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2016 and held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2016 ; Conference Date: 2 April 2016 Through 8 April 2016; Conference Code:173509
%< avec comité de lecture
%B Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2016. Lecture Notes in Computer Science
%C Eindhoven, Netherlands
%Y Raskin J.-F., Chechik M.
%I Springer Verlag
%V 9636
%P 225-243
%8 2016-04-08
%D 2016
%R 10.1007/978-3-662-49674-9_13
%K Calculations
%K Computation theory
%K Embedded systems
%K Concentration of measure
%K Cyber physical systems (CPSs)
%K Higher order moments
%K Nonlinear computations
%K Numerical computations
%K Precise tracking
%K Program variables
%K Uncertainty propagation
%K Probability distributions
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%L cea-01834974
%U https://cea.hal.science/cea-01834974
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