magi: 'magi': MAnifold-Constrained Gaussian Process Inference

In magi: MAnifold-Constrained Gaussian Process Inference

magi: MAnifold-Constrained Gaussian Process Inference

Description

magi is a package that provides fast and accurate inference for the parameter estimation problem in Ordinary Differential Equations, including the case when there are unobserved system components. In the references below, please see our software paper Wong, Yang, and Kou (2024) for a detailed user guide and Yang, Wong, and Kou (2021) for details of the MAGI method (MAnifold-constrained Gaussian process Inference).

References

Wong, S. W. K., Yang, S., & Kou, S. C. (2024). magi: A Package for Inference of Dynamic Systems from Noisy and Sparse Data via Manifold-Constrained Gaussian Processes. Journal of Statistical Software, 109 (4), 1-47. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v109.i04")}

Yang, S., Wong, S. W. K., & Kou, S. C. (2021). Inference of Dynamic Systems from Noisy and Sparse Data via Manifold-constrained Gaussian Processes. Proceedings of the National Academy of Sciences, 118 (15), e2020397118. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1073/pnas.2020397118")}

magi documentation built on June 22, 2024, 6:45 p.m.