Construct_W0_exp: covariance of the stationary distribution of the state when...
In FastGaSP: Fast and Exact Computation of Gaussian Stochastic Process
Description
Usage
Arguments
Value
Author(s)
References
Description
This function computes the covariance of the stationary distribution of the state when kernel is the exponential covariance.
Usage
1
Construct_W0_exp(sigma2,lambda)
Arguments
sigma2
the variance parameter.
lambda
the transformed range parameter.
Value
W0 matrix.
Author(s)
Mengyang Gu [aut, cre]
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 Sept. 5, 2021, 5:36 p.m.
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Description Usage Arguments Value Author(s) References
Description
This function computes the covariance of the stationary distribution of the state when kernel is the exponential covariance.
Usage
1 | Construct_W0_exp(sigma2,lambda)
|
Arguments
sigma2 |
the variance parameter. |
lambda |
the transformed range parameter. |
Value
W0 matrix.
Author(s)
Mengyang Gu [aut, cre]
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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