Get_Q_K

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

internal

Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It includes efficient implementations of the inverse Kalman filter, with applications such as estimating particle interaction functions. These tools support models with or without noise. Additionally, the package offers algorithms for fast parameter estimation in latent factor models, where the factor loading matrix is orthogonal, and latent processes are modeled by Gaussian processes. See the references: 1) Mengyang Gu and Yanxun Xu (2020), Journal of Computational and Graphical Statistics; 2) Xinyi Fang and Mengyang Gu (2024), <doi:10.48550/arXiv.2407.10089>; 3) Mengyang Gu and Weining Shen (2020), Journal of Machine Learning Research; 4) Yizi Lin, Xubo Liu, Paul Segall and Mengyang Gu (2025), <doi:10.48550/arXiv.2501.01324>.

Mengyang Gu

FastGaSP

Fast and Exact Computation of Gaussian Stochastic Process

Xinyi Fang

Yizi Lin

Get_Q_K function

<dl> <dt>GG</dt>
<dd>a list of matrices defined in the dynamic linear model.</dd> <dt>W</dt>
<dd>a list of matrices defined in the dynamic linear model.</dd> <dt>C0</dt>
<dd>a matrix defined in the dynamic linear model.</dd> <dt>VV</dt>
<dd>a numerical value for the nugget.</dd></dl>

Arguments

tools:::Rd_package_author("FastGaSP")
Maintainer: tools:::Rd_package_maintainer("FastGaSP")

Author

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

<dl>

 <dt>GG</dt>
<dd>a list of matrices defined in the dynamic linear model.</dd>

 <dt>W</dt>
<dd>a list of matrices defined in the dynamic linear model.</dd>

 <dt>C0</dt>
<dd>a matrix defined in the dynamic linear model.</dd>

 <dt>VV</dt>
<dd>a numerical value for the nugget.</dd>

</dl>

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

Description

Usage

Value

Arguments

Author

References