Construct_W_exp_fastGP: The conditional covariance matrix of the state in the dynamic...
In SKFCPD: Fast Online Changepoint Detection for Temporally Correlated Data
Construct_W_exp_fastGP 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_fastGP(delta_x,lambda,W0)
Arguments
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)
Hanmo Li [aut, cre],
Yuedong Wang [aut],
Mengyang Gu [aut]
Maintainer: Hanmo Li <hanmo@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.
SKFCPD documentation built on June 22, 2024, 11:06 a.m.
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| Construct_W_exp_fastGP | 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_fastGP(delta_x,lambda,W0)
Arguments
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)
Hanmo Li [aut, cre], Yuedong Wang [aut], Mengyang Gu [aut]
Maintainer: Hanmo Li <hanmo@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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