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Thinking Bayesian for plasma physicists

Journal Article · · Physics of Plasmas
DOI:https://doi.org/10.1063/5.0205668· OSTI ID:2567796

Bayesian statistics offers a powerful technique for plasma physicists to infer knowledge from the heterogeneous data types encountered. To explain this power, a simple example, Gaussian Process Regression, and the application of Bayesian statistics to inverse problems are explained. The likelihood is the key distribution because it contains the data model, or theoretic predictions, of the desired quantities. By using prior knowledge, the distribution of the inferred quantities of interest based on the data given can be inferred. Because it is a distribution of inferred quantities given the data and not a single prediction, uncertainty quantification is a natural consequence of Bayesian statistics. The benefits of machine learning in developing surrogate models for solving inverse problems are discussed, as well as progress in quantitatively understanding the errors that such a model introduces.

Research Organization:
General Atomics, San Diego, CA (United States); Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States); Tech-X Corporation, Boulder, CO (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Fusion Energy Sciences (FES)
Grant/Contract Number:
AC02-09CH11466; FC02-04ER54698; FG02-95ER54309; SC0021203; SC0021380
OSTI ID:
2567796
Alternate ID(s):
OSTI ID: 2395943
OSTI ID: 2484167
OSTI ID: 2377002
Journal Information:
Physics of Plasmas, Journal Name: Physics of Plasmas Journal Issue: 5 Vol. 31; ISSN 1070-664X
Publisher:
American Institute of PhysicsCopyright Statement
Country of Publication:
United States
Language:
English

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