Data-driven model reduction for the Bayesian solution of inverse problems

SummaryOne of the major challenges in the Bayesian solution of inverse problems governed by partial differential equations (PDEs) is the computational cost of repeatedly evaluating numerical PDE models, as required by Markov chain Monte Carlo (MCMC) methods for posterior sampling. This paper propose...

Celý popis

Podrobná bibliografie

Vydáno v:	International journal for numerical methods in engineering Ročník 102; číslo 5; s. 966 - 990
Hlavní autoři:	Cui, Tiangang, Marzouk, Youssef M., Willcox, Karen E.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bognor Regis Blackwell Publishing Ltd 04.05.2015 Wiley Subscription Services, Inc
Témata:	adaptive Markov chain Monte Carlo approximate Bayesian inference Bayesian analysis Computational efficiency Construction costs inverse problem Inverse problems Mathematical models Model reduction Partial differential equations Sampling
ISSN:	0029-5981, 1097-0207
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!