Efficient Monte Carlo Uncertainty Quantification through Problem-dependent Proposals
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
The solution of an inverse problem is a process where an algorithm asks questions to the data. In some cases the questions are yes/no questions (accepting or rejecting a model proposed by an Markov Chain Monte Carlo (MCMC) algorithm) and in other cases the questions are more complex, as in a deterministic algorithm's quest for gradients or curvatures. However, no algorithm can ask the right question without an efficient interrogation strategy. Such a strategy comes from what we call 'prior information', either about the solution to be found, or about the nature of the forward relation. The latter strategy is particularly important and is for MCMC algorithms expressed through the 'proposal distribution'. We shall explore the importance of proposal strategies, and show that dramatic improvements can be made if information-rich strategies are employed.
Original language | English |
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Title of host publication | 81st EAGE Conference and Exhibition 2019 Workshop Programme |
Publisher | EAGE Publishing BV |
Publication date | 2019 |
ISBN (Electronic) | 9789462822924 |
DOIs | |
Publication status | Published - 2019 |
Event | 81st EAGE Conference and Exhibition 2019 Workshop Programme - London, United Kingdom Duration: 3 Jun 2019 → 6 Jun 2019 |
Conference
Conference | 81st EAGE Conference and Exhibition 2019 Workshop Programme |
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Land | United Kingdom |
By | London |
Periode | 03/06/2019 → 06/06/2019 |
Series | 81st EAGE Conference and Exhibition 2019 Workshop Programme |
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ID: 241098353