get_mu_sigma_hat: Caculate the mean and variance parameter through fast...
In SKFCPD: Fast Online Changepoint Detection for Temporally Correlated Data
get_mu_sigma_hat R Documentation
Caculate the mean and variance parameter through fast computation
Description
This function computes the estimtation of the mean and variance parameter through Kalamn filters for fast computations.
Usage
get_mu_sigma_hat(param, design, response, kernel_type)
Arguments
param
a vector of parameters. The first parameter is the natural logarithm of the inverse range parameter in the kernel function. If the data contain noise, the second parameter is the logarithm of the nugget-variance ratio parameter.
design
A matrix with dimension n x p. The design of the experiment.
response
A matrix with dimension n x q. The observations.
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.
Value
A list with the estimtation of the mean and variance parameter.
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 (2017), Nonseparable Gaussian stochastic process: a unified
view and computational strategy, arXiv:1711.11501.
M. Gu, X. Wang and J.O. Berger (2018), Robust Gaussian Stochastic Process Emulation, Annals of Statistics, 46, 3038-3066.
SKFCPD documentation built on June 22, 2024, 11:06 a.m.
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.
| get_mu_sigma_hat | R Documentation |
Caculate the mean and variance parameter through fast computation
Description
This function computes the estimtation of the mean and variance parameter through Kalamn filters for fast computations.
Usage
get_mu_sigma_hat(param, design, response, kernel_type)
Arguments
param |
a vector of parameters. The first parameter is the natural logarithm of the inverse range parameter in the kernel function. If the data contain noise, the second parameter is the logarithm of the nugget-variance ratio parameter. |
design |
A matrix with dimension n x p. The design of the experiment. |
response |
A matrix with dimension n x q. The observations. |
kernel_type |
A character specifying the type of kernels of the input. |
Value
A list with the estimtation of the mean and variance parameter.
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 (2017), Nonseparable Gaussian stochastic process: a unified view and computational strategy, arXiv:1711.11501.
M. Gu, X. Wang and J.O. Berger (2018), Robust Gaussian Stochastic Process Emulation, Annals of Statistics, 46, 3038-3066.
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.