# Field Dynamics Inference for Local and Causal Interactions

@article{Frank2019FieldDI, title={Field Dynamics Inference for Local and Causal Interactions}, author={Philipp Frank and Reimar H. Leike and Torsten A. Ensslin}, journal={Annalen der Physik}, year={2019}, volume={533} }

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…

## 4 Citations

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The proposed Metric Gaussian Variational Inference (MGVI) is an iterative method that performs a series of Gaussian approximations to the posterior that achieves linear scaling by avoiding to store the covariance explicitly at any time.

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This work proposes geometric Variational Inference (geoVI), a method based on Riemannian geometry and the Fisher information metric that is used to construct a coordinate transformation that relates the RiemANNian manifold associated with the metric to Euclidean space.

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