GaSP_CPD_pred_dist_objective_prior_direct_online {SKFCPD} | R Documentation |
Computing the predictive distribution directly in the online fashion
Description
This function computs directly the predictive distribution of the run length in the online fashion. The direct computation includes the inversion of covariance matrix, which is of computational complexity $O(n^3)$, with $n$ being the number of observations.
Usage
GaSP_CPD_pred_dist_objective_prior_direct_online(cur_seq, d, gamma, eta, mu, sigma_2)
Arguments
cur_seq |
A vector of sequence of observations. |
d |
A value of the distance between the sorted input. |
gamma |
A numeric variable of the range parameter for the covariance matrix. The default value of gamma is 1. |
eta |
A vector of the noise-to-signal ratio at each coordinate |
mu |
A vector of the mean parameter at each coordinate. Ignored when model_type = 0 or 2. |
sigma_2 |
A vector of the variance parameter at each coordinate. |
Value
GaSP_CPD_pred_dist_objective_prior_direct_online
returns the log likelihood of observations that follows Gaussian Process with Exponential kernel.
Author(s)
Hanmo Li [aut, cre], Yuedong Wang [aut], Mengyang Gu [aut]
Maintainer: Hanmo Li <hanmo@pstat.ucsb.edu>
References
Williams, C. K., & Rasmussen, C. E. (2006). Gaussian processes for machine learning (Vol. 2, No. 3, p. 4). Cambridge, MA: MIT press.