Record Details
Sampling, feasibility, and priors in Bayesian estimation
ScholarsArchive at Oregon State University
| Field | Value |
|---|---|
| Title | Sampling, feasibility, and priors in Bayesian estimation |
| Names |
Chorin, Alexandre J.
(creator) Lu, Fei (creator) Miller, Robert N. (creator) Morzfeld, Matthias (creator) Tu, Xuemin (creator) |
| Date Issued | 2016-08 (iso8601) |
| Note | To the best of our knowledge, one or more authors of this paper were federal employees when contributing to this work. This is the publisher’s final pdf. The published article is copyrighted by American Institute of Mathematical Sciences and can be found at: http://www.aimsciences.org/journals/home.jsp?journalID=1 |
| Abstract | Importance sampling algorithms are discussed in detail, with an emphasis on implicit sampling, and applied to data assimilation via particle filters. Implicit sampling makes it possible to use the data to find high-probability samples at relatively low cost, making the assimilation more efficient. A new analysis of the feasibility of data assimilation is presented, showing in detail why feasibility depends on the Frobenius norm of the covariance matrix of the noise and not on the number of variables. A discussion of the convergence of particular particle filters follows. A major open problem in numerical data assimilation is the determination of appropriate priors; a progress report on recent work on this problem is given. The analysis highlights the need for a careful attention both to the data and to the physics in data assimilation problems. |
| Genre | Article |
| Topic | Monte Carlo |
| Identifier | Chorin, A. J., Lu, F., Miller, R. N., Morzfeld, M., & Tu, X. (2015). Sampling, feasibility, and priors in Bayesian estimation. Discrete and Continuous Dynamical Systems - Series A, 36(8), 4227-4246. doi:10.3934/dcds.2016.8.4227 |
