KF_ini_for_profile_like {SKFCPD} | R Documentation |
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. |
G_W_W0_V |
A list of the coefficient and conditional matrics for Gaussian Process(GP) model. It's the output from the function |
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 <hanmo@pstat.ucsb.edu>
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.