Dimension-independent likelihood-informed MCMC

Dimension-independent likelihood-informed MCMC

Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters that represent the discretization of an underlying function. This work introduces a family of Markov chain Monte Carlo (MCMC) samplers that can adapt to the particular structure of a posterio...

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Bibliographic Details
Main Authors: Law, Kody J.H., Cui, Tiangang, Marzouk, Youssef M
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Format: Article
Published: Elsevier BV 2018
Online Access:http://hdl.handle.net/1721.1/114545
https://orcid.org/0000-0002-4840-8545
https://orcid.org/0000-0001-8242-3290

Internet

http://hdl.handle.net/1721.1/114545
https://orcid.org/0000-0002-4840-8545
https://orcid.org/0000-0001-8242-3290

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