Construct_G_W_W0_V: Generating coefficient and conditional matrics
In SKFCPD: Fast Online Changepoint Detection for Temporally Correlated Data
Construct_G_W_W0_V R Documentation
Generating coefficient and conditional matrics
Description
Generating coefficient and conditional matrics for Gaussian Process(GP) model with Matern 2.5 or power exponential kernels.
Usage
Construct_G_W_W0_V(d, gamma, eta, kernel_type, is_initial)
Arguments
d
A value of the distance between the sorted input.
gamma
A value of the range parameter for the covariance matrix.
eta
The noise-to-signal ratio.
kernel_type
A character specifying the type of kernels of the input. matern_5_2 are Matern correlation with roughness parameter 5/2. exp is power exponential correlation with roughness parameter alpha=2. The default choice is matern_5_2.
is_initial
A bolean variable. is_initial=TRUE means the matrics generated is for the inital state.
Value
A list of GG, W, W0 and VV 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_G_W_W0_V | R Documentation |
Generating coefficient and conditional matrics
Description
Generating coefficient and conditional matrics for Gaussian Process(GP) model with Matern 2.5 or power exponential kernels.
Usage
Construct_G_W_W0_V(d, gamma, eta, kernel_type, is_initial)
Arguments
d |
A value of the distance between the sorted input. |
gamma |
A value of the range parameter for the covariance matrix. |
eta |
The noise-to-signal ratio. |
kernel_type |
A character specifying the type of kernels of the input. |
is_initial |
A bolean variable. is_initial=TRUE means the matrics generated is for the inital state. |
Value
A list of GG, W, W0 and VV 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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