get_predictive_dist_direct_objective_prior: Updating the predictive distribution

In SKFCPD: Fast Online Changepoint Detection for Temporally Correlated Data

Updating the predictive distribution

Description

Updating the predictive distribution of the run length under the objective prior directly.

Usage

  get_predictive_dist_direct_objective_prior(cur_input_seq, d, gamma, mu, sigma_2, eta)

Arguments

cur_input_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

get_predictive_dist_direct_objective_prior 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 <[e-mail]>

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.