Multilevel Delayed Acceptance MCMC-Reference-Cited by-同舟云学术

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Multilevel Delayed Acceptance MCMC

Published:2023-01-25 Issue:1 Volume:11 Page:1-30
ISSN:2166-2525
Container-title:SIAM/ASA Journal on Uncertainty Quantification
language:en
Short-container-title:SIAM/ASA J. Uncertainty Quantification

Author:

Lykkegaard M. B.¹^ORCID,Dodwell T. J.²,Fox C.³^ORCID,Mingas G.⁴,Scheichl R.⁵

Affiliation:

1. Centre for Water Systems and Institute for Data Science and AI University of Exeter, Exeter EX4 4QF, UK.

2. Alan Turing Institute and Institute for Data Science and AI, University of Exeter, Exeter EX4 4QF, UK.

3. Department of Physics, University of Otago, Dunedin 9016, New Zealand.

4. Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK.

5. Institute for Applied Mathematics and Interdisciplinary Center for Scientific Computing, Heidelberg University, 69120 Heidelberg, Germany.

Funder

Deutsche Forschungsgemeinschaft

Ministry for Business Innovation and Employment

Engineering and Physical Sciences Research Council

Alan Turing Institute

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Discrete Mathematics and Combinatorics,Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference49 articles.

1. A tutorial on adaptive MCMC

2. An Adaptive Version for the Metropolis Adjusted Langevin Algorithm with a Truncated Drift

3. A Short History of Mathematical Population Dynamics

4. Multi-level Monte Carlo Finite Element method for elliptic PDEs with stochastic coefficients

5. Geometric MCMC for infinite-dimensional inverse problems

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1. Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models;Advances in Computational Mathematics;2024-07-03

2. A BAYESIAN CALIBRATION FRAMEWORK WITH EMBEDDED MODEL ERROR FOR MODEL DIAGNOSTICS;International Journal for Uncertainty Quantification;2024

3. Separating internal and externally forced contributions to global temperature variability using a Bayesian stochastic energy balance framework;Chaos: An Interdisciplinary Journal of Nonlinear Science;2022-11

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