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Emulation of Higher-Order Tensors in Manifold Monte Carlo Methods for Bayesian Inverse Problems

22 July 2015

Papers citing "Emulation of Higher-Order Tensors in Manifold Monte Carlo Methods for Bayesian Inverse Problems"

23 / 23 papers shown

Title
Are Statistical Methods Obsolete in the Era of Deep Learning? Skyler Wu Shihao Yang S. C. Kou 19 0 0 27 May 2025
Sacred and Profane: from the Involutive Theory of MCMC to Helpful Hamiltonian Hacks N. Glatt-Holtz Andrew J. Holbrook J. Krometis Cecilia F. Mondaini Ami D. Sheth 63 0 0 22 Oct 2024
Warped geometric information on the optimisation of Euclidean functions M. Hartmann Bernardo Williams Hanlin Yu Mark Girolami Alessandro Barp Arto Klami 81 3 0 16 Aug 2023
On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2 G. Didier N. Glatt-Holtz Andrew J Holbrook Andrew F. Magee M. Suchard 57 4 0 28 Jun 2023
Random-effects substitution models for phylogenetics via scalable gradient approximations Andrew F. Magee Andrew J Holbrook J. Pekar Itzue W. Caviedes-Solis IV FredrickA.Matsen G. Baele J. Wertheim Xiang Ji P. Lemey M. Suchard 41 5 0 23 Mar 2023
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations Zhao-Xia Li Shih-Feng Yang Jeff Wu 104 2 0 22 Dec 2022
Variational Inference for Nonlinear Inverse Problems via Neural Net Kernels: Comparison to Bayesian Neural Networks, Application to Topology Optimization Vahid Keshavarzzadeh Robert M. Kirby A. Narayan BDL 57 2 0 07 May 2022
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC Marko Jarvenpaa J. Corander 61 5 0 08 Apr 2021
Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods Oliver R. A. Dunbar Andrew B. Duncan Andrew M. Stuart Marie-Therese Wolfram 82 27 0 07 Apr 2021
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks Shiwei Lan Shuyi Li Babak Shahbaba UQCV BDL 84 16 0 11 Jan 2021
On the accept-reject mechanism for Metropolis-Hastings algorithms N. Glatt-Holtz J. Krometis Cecilia F. Mondaini 93 10 0 09 Nov 2020
Probabilistic Gradients for Fast Calibration of Differential Equation Models Jon Cockayne Andrew B. Duncan 63 5 0 03 Sep 2020
Calibrate, Emulate, Sample Emmet Cleary A. Garbuno-Iñigo Shiwei Lan T. Schneider Andrew M. Stuart 112 105 0 10 Jan 2020
Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success R. Scalzo D. Kohn H. Olierook G. Houseman Rohitash Chandra Mark Girolami Sally Cripps 57 32 0 02 Dec 2018
Adaptive Dimension Reduction to Accelerate Infinite-Dimensional Geometric Markov Chain Monte Carlo Shiwei Lan 74 10 0 15 Jul 2018
Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography L. Bruder P. Koutsourelakis MedIm AI4CE 42 9 0 02 Mar 2018
Neural Network Gradient Hamiltonian Monte Carlo Lingge Li Andrew J Holbrook Babak Shahbaba Pierre Baldi BDL 67 23 0 14 Nov 2017
Geometry and Dynamics for Markov Chain Monte Carlo Alessandro Barp François‐Xavier Briol A. Kennedy Mark Girolami AI4CE 85 31 0 08 May 2017
Geometric adaptive Monte Carlo in random environment Theodore Papamarkou Alexey Lindo E. Ford 43 4 0 29 Aug 2016
Geometric MCMC for Infinite-Dimensional Inverse Problems A. Beskos Mark Girolami Shiwei Lan P. Farrell Andrew M. Stuart 92 144 0 20 Jun 2016
Variational Hamiltonian Monte Carlo via Score Matching Cheng Zhang Babak Shahbaba Hongkai Zhao BDL 95 26 0 06 Feb 2016
Probabilistic Integration: A Role in Statistical Computation? François‐Xavier Briol Chris J. Oates Mark Girolami Michael A. Osborne Dino Sejdinovic 161 53 0 03 Dec 2015
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases Cheng Zhang Babak Shahbaba Hongkai Zhao 137 32 0 18 Jun 2015