get_LY_online: Updating Kalman filter parameters
In SKFCPD: Fast Online Changepoint Detection for Temporally Correlated Data

get_LY_online

R Documentation

Updating Kalman filter parameters

Description

Updating the Kalman filter parameters for Gaussian Process model with Matern 2.5 or power exponential kernels.

Usage

  get_LY_online(cur_input, prev_param, eta, G_W_W0_V)

Arguments

`cur_input`	A value of current observation.
`prev_param`	A list of previous Kalman filter parameters.
`eta`	The noise-to-signal ratio.
`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

get_LY_online returns a list of updated kalman filter parameters.

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.

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