GaSP_CPD_pred_dist_objective_prior_direct_online: Computing the predictive distribution directly in the online...

View source: R/RcppExports.R

GaSP_CPD_pred_dist_objective_prior_direct_onlineR 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.


SKFCPD documentation built on June 22, 2024, 11:06 a.m.