Piecewise polynomial approximation of probability density functions with application to uncertainty quantification for stochastic PDEs
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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 and are centered at the bin nodes rather than at the samples, as is the case for the standard kernel density estimation approach. This feature provides an approximation that is scalable with respect to the sample size. On the other hand, unlike other strategies that place kernel functions at bin nodes, the new 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 new density estimator has unitary integral, does not require a constraint to enforce positivity, and is consistent. The new 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.
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