Construct_G_matern_5_2: The coefficient matrix in the dynamic linear model when...
In FastGaSP: Fast and Exact Computation of Gaussian Stochastic Process
Construct_G_matern_5_2 R Documentation
The coefficient matrix in the dynamic linear model when kernel is the Matern covariance with roughness parameter 2.5.
Description
The coefficient matrix in the dynamic linear model when kernel is the Matern covariance with roughness parameter 2.5.
Usage
Construct_G_matern_5_2(delta_x,lambda)
Arguments
delta_x
The distance between the sorted input.
lambda
the transformed range parameter.
Value
GG matrix.
Author(s)
Mengyang Gu [aut, cre],
Xinyi Fang [aut],
Yizi Lin [aut]
Maintainer: Mengyang Gu <mengyang@pstat.ucsb.edu>
References
Hartikainen, J. and Sarkka, S. (2010). Kalman filtering and smoothing solutions to temporal gaussian process regression models. Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop, 379-384.
M. Gu, Y. Xu (2019), fast nonseparable Gaussian stochastic process with application to methylation level interpolation. Journal of Computational and Graphical Statistics, In Press, arXiv:1711.11501.
Campagnoli P, Petris G, Petrone S. (2009), Dynamic linear model with R. Springer-Verlag New York.
FastGaSP documentation built on April 4, 2025, 5:16 a.m.
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| Construct_G_matern_5_2 | R Documentation |
The coefficient matrix in the dynamic linear model when kernel is the Matern covariance with roughness parameter 2.5.
Description
The coefficient matrix in the dynamic linear model when kernel is the Matern covariance with roughness parameter 2.5.
Usage
Construct_G_matern_5_2(delta_x,lambda)
Arguments
delta_x |
The distance between the sorted input. |
lambda |
the transformed range parameter. |
Value
GG matrix.
Author(s)
Mengyang Gu [aut, cre], Xinyi Fang [aut], Yizi Lin [aut]
Maintainer: Mengyang Gu <mengyang@pstat.ucsb.edu>
References
Hartikainen, J. and Sarkka, S. (2010). Kalman filtering and smoothing solutions to temporal gaussian process regression models. Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop, 379-384.
M. Gu, Y. Xu (2019), fast nonseparable Gaussian stochastic process with application to methylation level interpolation. Journal of Computational and Graphical Statistics, In Press, arXiv:1711.11501.
Campagnoli P, Petris G, Petrone S. (2009), Dynamic linear model with R. Springer-Verlag New York.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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