GaSP_CPD_pred_dist_objective_prior_KF_online: Computing the predictive distribution in the online fashion

In SKFCPD: Fast Online Changepoint Detection for Temporally Correlated Data

Computing the predictive distribution in the online fashion

Description

This function computs the predictive distribution of the run length in the online fashion.

Usage

  GaSP_CPD_pred_dist_objective_prior_KF_online(KF_params, prev_L_params, cur_point,
  d, gamma, model_type, mu, sigma_2, eta, kernel_type, G_W_W0_V_ini, G_W_W0_V)

Arguments

KF_params	A list of current Kalman filter parameters.
prev_L_params	A list of previous Kalman filter parameters.
cur_point	A value of current observation.
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.
model_type	A numeric variable that can take values of 0, 1 and 2. Model_type=0 stands for a GP model with unknown mean and known variance. Model_type=1 stands for a GP model with known mean and unknown variance. Model_type=2 stands for a GP model with unknown mean and unknown variance. The default value of model_type is 2.
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.
eta	A vector of the noise-to-signal ratio at each coordinate
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.
G_W_W0_V_ini	A list of the initial coefficient and conditional matrics for Gaussian Process(GP) model. It's the output from the function Construct_G_W_W0_V
G_W_W0_V	A list of the coefficient and conditional matrics for Gaussian Process(GP) model. It's the output from the function Construct_G_W_W0_V

Value

GaSP_CPD_pred_dist_objective_prior_KF_online returns a list that contains 3 items: (1) the current Kalman filter parameters; (2) the previous Kalman filter parameters and (3) the vector of the logrithm for the current predictive distribution of different run lengths.

Author(s)

Hanmo Li [aut, cre], Yuedong Wang [aut], Mengyang Gu [aut]

Maintainer: Hanmo Li <[e-mail]>

References

Fearnhead, P., & Liu, Z. (2007). On-line inference for multiple changepoint problem. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(4), 589-605.

Adams, R. P., & MacKay, D. J. (2007). Bayesian online changepoint detection. arXiv preprint arXiv:0710.3742.

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.

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