期刊
ANNALEN DER PHYSIK
卷 533, 期 5, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/andp.202000486
关键词
Bayesian methods; data analysis; information theory; stochastic processes
资金
- Projekt DEAL
Inference of fields defined in space and time from observational data is a core discipline in many scientific areas. The proposed method in this work is based on statistically homogeneous random fields defined in space and time and demonstrates how to reconstruct the field together with its prior correlation structure from data. The prior model of the correlation structure is described in a non-parametric fashion and solely builds on fundamental physical assumptions such as space-time homogeneity, locality, and causality.
Inference of fields defined in space and time from observational data is a core discipline in many scientific areas. This work approaches the problem in a Bayesian framework. The proposed method is based on statistically homogeneous random fields defined in space and time and demonstrates how to reconstruct the field together with its prior correlation structure from data. The prior model of the correlation structure is described in a non-parametric fashion and solely builds on fundamental physical assumptions such as space-time homogeneity, locality, and causality. These assumptions are sufficient to successfully infer the field and its prior correlation structure from noisy and incomplete data of a single realization of the process as demonstrated via multiple numerical examples.
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