Bayesian model inversion using stochastic spectral embedding
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Publication:2131057
DOI10.1016/j.jcp.2021.110141OpenAlexW3024020114WikidataQ128176593 ScholiaQ128176593MaRDI QIDQ2131057
Bruno Sudret, Stefano Marelli, Paul-Remo Wagner
Publication date: 25 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2005.07380
inverse problemspolynomial chaos expansionsBayesian model inversionsampling-free inversionspectral likelihood expansionsstochastic spectral likelihood embedding
Statistics (62-XX) Parametric inference (62Fxx) Probabilistic methods, stochastic differential equations (65Cxx)
Related Items (4)
Bayesian inversion using adaptive polynomial chaos kriging within subset simulation ⋮ An hp‐adaptive multi‐element stochastic collocation method for surrogate modeling with information re‐use ⋮ Multielement polynomial chaos kriging-based metamodelling for Bayesian inference of non-smooth systems ⋮ Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors
Uses Software
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