cea-01834974
https://cea.hal.science/cea-01834974
doi:10.1007/978-3-662-49674-9_13
[CEA] CEA - Commissariat à l'énergie atomique
[X] Ecole Polytechnique
[CNRS] CNRS - Centre national de la recherche scientifique
[LIX] Laboratoire d'informatique de l'école polytechnique
[X-LIX] Laboratoire d'informatique de l'X (LIX)
[X-DEP] Polytechnique
[X-DEP-INFO] Département d'informatique
[DRT] Direction de la recherche technologique
[CEA-UPSAY] CEA - Université Paris-Saclay
[UNIV-PARIS-SACLAY] Université Paris-Saclay
[CEA-UPSAY-SACLAY] CEA-UPSAY-SACLAY
[X-SACLAY] X-SACLAY
[LIST] Laboratoire d'Intégration des Systèmes et des Technologies
[GS-ENGINEERING] Graduate School Sciences de l'Ingénierie et des Systèmes
[GS-COMPUTER-SCIENCE] Graduate School Computer Science
Uncertainty propagation using probabilistic affine forms and concentration of measure inequalities
Bouissou, O.
Goubault, E.
Putot, S.
Chakarov, A.
Sankaranarayanan, S.
[INFO] Computer Science [cs]
COMM
Calculations
Computation theory
Embedded systems
Concentration of measure
Cyber physical systems (CPSs)
Higher order moments
Nonlinear computations
Numerical computations
Precise tracking
Program variables
Uncertainty propagation
Probability distributions
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.
2016-04-08
en
Springer Verlag
Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2016. Lecture Notes in Computer Science
Eindhoven, Netherlands