A Bayesian Approach for Spatio-Temporal Data-Driven Dynamic Equation Discovery-Reference-Cited by-同舟云学术

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A Bayesian Approach for Spatio-Temporal Data-Driven Dynamic Equation Discovery

Published:2023-01-01 Issue:-1 Volume:-1 Page:
ISSN:1936-0975
Container-title:Bayesian Analysis
language:
Short-container-title:Bayesian Anal.

Author:

North Joshua S.¹,Wikle Christopher K.²,Schliep Erin M.³

Affiliation:

1. Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 1 Cyclotron Road, USA

2. Department of Statistics, University of Missouri, Columbia, MO, 146 Middlebush Hall, USA

3. Department of Statistics, North Carolina State University, Raleigh, NC, 2311 Stinson Drive, USA

Publisher

Institute of Mathematical Statistics

Subject

Applied Mathematics,Statistics and Probability

Reference72 articles.

1. Tibshirani, R. (1996). “Regression Shrinkage and Selection Via the Lasso.” Journal of the Royal Statistical Society: Series B (Methodological), 58(1): 267–288.

2. Bateman, H. (1915). “Some recent researches on the motion of fluids.” Monthly Weather Review, 43(4): 163–170.

3. Berliner, L. M. (1996). “Hierarchical Bayesian time series models.” In Maximum Entropy and Bayesian Methods, 15–22. Dordrecht: Springer Netherlands.

4. Berliner, L. M. (2003). “Physical-statistical modeling in geophysics.” Journal of Geophysical Research: Atmospheres, 108(D24).

5. Bolker, B. and Grenfell, B. (1995). “Space, persistence and dynamics of measles epidemics.” Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 348(1325): 309–320.

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Review of Data‐Driven Discovery for Dynamic Systems;International Statistical Review;2023-09-29

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