Conference item
QMC sampling from empirical datasets
- Abstract:
- This paper presents a simple idea for the use of quasi-Monte Carlo sampling with empirical datasets, such as those generated by MCMC methods. It also presents and analyses a related idea of taking advantage of the Hilbert space-filling curve. Theoretical and numerical analyses are provided for both. We find that when applying the proposed QMC sampling methods to datasets coming from a known distribution, they give similar performance as the standard QMC method directly sampling from this known distribution.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, 209.1KB, Terms of use)
-
- Publisher copy:
- 10.1007/978-3-030-43465-6_26
Authors
- Publisher:
- Springer
- Host title:
- Monte Carlo and Quasi Monte Carlo Methods in Scientific Computing
- Pages:
- 523-539
- Series:
- Springer Proceedings in Mathematics & Statistics
- Series number:
- 324
- Publication date:
- 2020-05-02
- Acceptance date:
- 2019-09-05
- Event title:
- Monte Carlo and Quasi-Monte Carlo Methods (MCQMC 2018)
- Event location:
- Rennes, France
- Event website:
- http://mcqmc2018.inria.fr/proceedings/
- Event start date:
- 2018-07-01
- Event end date:
- 2018-07-06
- DOI:
- EISBN:
- 9783030434656
- ISBN:
- 9783030434649
- Language:
-
English
- Keywords:
- Pubs id:
-
1127994
- Local pid:
-
pubs:1127994
- Deposit date:
-
2020-09-03
Terms of use
- Copyright holder:
- Springer Nature
- Copyright date:
- 2020
- Rights statement:
- © Springer Nature Switzerland AG 2020.
- Notes:
- This paper was presented at the Monte Carlo and Quasi-Monte Carlo Methods (MCQMC 2018), Rennes, France, July 2018. This is the accepted manuscript version of the paper. The final version is available online from Springer at: https://doi.org/10.1007/978-3-030-43465-6_26
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