Get_Q_K: one-step-ahead predictive variance and Kalman gain

View source: R/RcppExports.R

Get_Q_KR Documentation

one-step-ahead predictive variance and Kalman gain

Description

This function computes the one-step-ahead predictive variance and Kalman gain.

Usage

Get_Q_K(GG,W,C0,VV)

Arguments

GG

a list of matrices defined in the dynamic linear model.

W

a list of matrices defined in the dynamic linear model.

C0

a matrix defined in the dynamic linear model.

VV

a numerical value for the nugget.

Value

A list of 2 items for Q and K.

Author(s)

Mengyang Gu [aut, cre], Xinyi Fang [aut], Yizi Lin [aut]

Maintainer: Mengyang Gu <mengyang@pstat.ucsb.edu>

References

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.

M. Gu, Y. Xu (2019), fast nonseparable gaussian stochastic process with application to methylation level interpolation. Journal of Computational and Graphical Statistics, In Press, arXiv:1711.11501.

Campagnoli P, Petris G, Petrone S. (2009), Dynamic linear model with R. Springer-Verlag New York.


FastGaSP documentation built on April 4, 2025, 5:16 a.m.