Sample_KF: Sample the prior process using a dynamic linear model

In FastGaSP: Fast and Exact Computation of Gaussian Stochastic Process

Sample the prior process using a dynamic linear model

Description

This function samples the piror process using a dynamic liner model.

Usage

Sample_KF(GG,W,C0,VV,kernel_type,sample_type)

Arguments

GG	a list of matrices defined in the dynamic linear model.
W	a list of coefficient matrices defined in the dynamic linear model.
C0	the covariance matrix of the stationary distribution defined in the dynamic linear model.
VV	a numerical value of the variance of the nugget parameter.
kernel_type	a character to specify the type of kernel to use. The current version supports kernel_type to be "matern_5_2" or "exp", meaning that the matern kernel with roughness parameter being 2.5 or 0.5 (exponent kernel), respectively.
sample_type	a integer to specify the type of sample we need. 0 means the states. 1 means the first value of each state vector. 2 means the noisy observations.

Value

A matrix of the samples.

Author(s)

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

Maintainer: Mengyang Gu <[e-mail]>

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.

See Also

Sample_KF_post for more details about sampling from the posterior distribution.

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