get_Q_K: matrices and vectors for the inverse covariance in the...
In SKFCPD: Fast Online Changepoint Detection for Temporally Correlated Data
Get_Q_K R Documentation
matrices and vectors for the inverse covariance in the predictive distribution
Description
This function computes the required values for the inverse covariance matrix.
Usage
Get_Q_K(GG,W,C0,VV)
Arguments
GG
a list of matrices defined in the dynamic linear model.
W
a list of matrices defined in the dynamic linear model.
C0
a matrix defined in the dynamic linear model.
VV
a numerical value for the nugget.
Value
A list of 2 items for Q and K.
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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| Get_Q_K | R Documentation |
matrices and vectors for the inverse covariance in the predictive distribution
Description
This function computes the required values for the inverse covariance matrix.
Usage
Get_Q_K(GG,W,C0,VV)
Arguments
GG |
a list of matrices defined in the dynamic linear model. |
W |
a list of matrices defined in the dynamic linear model. |
C0 |
a matrix defined in the dynamic linear model. |
VV |
a numerical value for the nugget. |
Value
A list of 2 items for Q and K.
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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