Construct_W_exp: The conditional covariance matrix of the state in the dynamic...
In FastGaSP: Fast and Exact Computation of Gaussian Stochastic Process
Construct_W_exp R Documentation
The conditional covariance matrix of the state in the dynamic linear model when kernel is the exponential covariance
Description
The conditional covariance matrix of the state in the dynamic linear model when kernel is the exponential covariance.
Usage
Construct_W_exp(sigma2,delta_x,lambda,W0)
Arguments
sigma2
the variance parameter.
delta_x
the distance between the sorted input.
lambda
the transformed range parameter.
W0
the covariance matrix of the stationary distribution of the state.
Value
W 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 May 29, 2024, 1:30 a.m.
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| Construct_W_exp | R Documentation |
The conditional covariance matrix of the state in the dynamic linear model when kernel is the exponential covariance
Description
The conditional covariance matrix of the state in the dynamic linear model when kernel is the exponential covariance.
Usage
Construct_W_exp(sigma2,delta_x,lambda,W0)
Arguments
sigma2 |
the variance parameter. |
delta_x |
the distance between the sorted input. |
lambda |
the transformed range parameter. |
W0 |
the covariance matrix of the stationary distribution of the state. |
Value
W 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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