compute_log_lik: Natural logarithm of profile likelihood by the fast computing...
In SKFCPD: Fast Online Changepoint Detection for Temporally Correlated Data
compute_log_lik R Documentation
Natural logarithm of profile likelihood by the fast computing algorithm
Description
This function computes the natural logarithm of the profile likelihood for the range and nugget parameter after plugging the closed form maximum likelihood estimator for the variance parameter.
Usage
compute_log_lik(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
The numerical value of natural logarithm of the profile likelihood.
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.
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| compute_log_lik | R Documentation |
Natural logarithm of profile likelihood by the fast computing algorithm
Description
This function computes the natural logarithm of the profile likelihood for the range and nugget parameter after plugging the closed form maximum likelihood estimator for the variance parameter.
Usage
compute_log_lik(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
The numerical value of natural logarithm of the profile likelihood.
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.
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