Piecewise Polynomial Approximation of Probability Density Functions with Application to Uncertainty Quantification for Stochastic PDEs

  • Giacomo Capodaglio
  • Max GunzburgerEmail author
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 137)


The probability density function (PDF) associated with a given set of samples is approximated by a piecewise-linear polynomial constructed with respect to a binning of the sample space. The kernel functions are a compactly supported basis for the space of such polynomials, i.e. finite element hat functions, that are centered at the bin nodes rather than at the samples, as is the case for the standard kernel density estimation approach. This feature naturally provides an approximation that is scalable with respect to the sample size. On the other hand, unlike other strategies that use a finite element approach, the proposed approximation does not require the solution of a linear system. In addition, a simple rule that relates the bin size to the sample size eliminates the need for bandwidth selection procedures. The proposed density estimator has unitary integral, does not require a constraint to enforce positivity, and is consistent. The proposed approach is validated through numerical examples in which samples are drawn from known PDFs. The approach is also used to determine approximations of (unknown) PDFs associated with outputs of interest that depend on the solution of a stochastic partial differential equation.


Stochastic PDEs Uncertainty quantification Probability density functions Polynomial approximation 



This work was supported by US Air Force Office of Scientific Research grant FA9550-15-1-0001 and by the Sandia National Laboratories contract 1985151.


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Copyright information

© National Technology & Engineering Solutions of Sandia, and The Editor(s), under exclusive license to Springer Nature Switzerland AG  2020

Authors and Affiliations

  1. 1.Department of Scientific ComputingFlorida State UniversityTallahasseeUSA

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