BIMC: The Bayesian Inverse Monte Carlo method for goal-oriented uncertainty quantification. Part I

2 November 2019

Papers citing "BIMC: The Bayesian Inverse Monte Carlo method for goal-oriented uncertainty quantification. Part I"

7 / 7 papers shown

Title
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs; Part I: Deterministic Inversion and Linearized Bayesian Inference Umberto Villa N. Petra Omar Ghattas 31 61 0 09 Sep 2019
Multifidelity probability estimation via fusion of estimators Boris Kramer A. Marques Benjamin Peherstorfer Umberto Villa Karen E. Willcox 35 25 0 07 May 2019
Langevin Incremental Mixture Importance Sampling Matteo Fasiolo Flávio Eler De Melo Simon Maskell 28 13 0 21 Nov 2016
Importance Sampling and Necessary Sample Size: an Information Theory Approach D. Sanz-Alonso 37 32 0 31 Aug 2016
Goal-oriented optimal approximations of Bayesian linear inverse problems Alessio Spantini Tiangang Cui Karen E. Willcox L. Tenorio Youssef Marzouk 45 33 0 07 Jul 2016
Importance Sampling: Intrinsic Dimension and Computational Cost S. Agapiou O. Papaspiliopoulos D. Sanz-Alonso Andrew M. Stuart 64 161 0 19 Nov 2015
Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet T. Isaac N. Petra G. Stadler Omar Ghattas PINN 54 153 0 05 Oct 2014