KF_ini_for_profile_like: Getting inital Kalman filter parameters for different...

In SKFCPD: Fast Online Changepoint Detection for Temporally Correlated Data

Getting inital Kalman filter parameters for different observation sequences

Description

Initialize the Kalman filter parameters for Gaussian Process model with Matern 2.5 or power exponential kernels with different observation sequences.

Usage

  KF_ini_for_profile_like(cur_input, d, gamma, eta, kernel_type, G_W_W0_V)

Arguments

cur_input	A value of current observation.
d	A value of the distance between the sorted input.
gamma	A value of the range parameter for the covariance matrix.
eta	The noise-to-signal ratio.
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.
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

KF_ini_for_profile_like returns a list of kalman filter parameters with different observation sequences.

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.