Get_L_inv_y: vector of half of the sum of squares
In FastGaSP: Fast and Exact Computation of Gaussian Stochastic Process

Description Usage Arguments Value Author(s) References See Also

This function computes the inverse of L matrix multiples the output vector, where the L matrix is the Cholesky decomposition of the correlation matrix. Instead of computing the Cholesky matrix, we compute it using the forward filtering algorithm.

1	Get_L_inv_y(GG,VV,Q,K,output)

`GG`	a list of matrices defined in the dynamic linear model.
`VV`	a numerical value of the variance of the nugget parameter.

`Q`	a vector defined in the dynamic linear model.
`K`	a matrix defined in the filtering algorithm for the dynamic linear model.
`output`	a vector of output.

A vector of the inverse of L matrix multiples the output vector, where the L matrix is the Cholesky decomposition of the correlation matrix.

Mengyang Gu [aut, cre]

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

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.

Get_C_R_K_Q for more details about Q vector and K matrix.

FastGaSP documentation built on Sept. 5, 2021, 5:36 p.m.