Sample_KF_post: Sample the posterior distribution of the process using the...
In FastGaSP: Fast and Exact Computation of Gaussian Stochastic Process

Description Usage Arguments Value Author(s) References See Also

This function samples the posterior distribution of the process using the backward smoothing algorithm.

1	Sample_KF_post(index_obs, C_R_K_Q,W0,GG,W,VV,output,kernel_type,sample_type)

`index_obs`	a vector where the entries with 1 have observations and entries with 0 have no observation.
`C_R_K_Q`	a list of matrices to compute the inverse covariance matrix in the dynamic linear model.
`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.
`output`	a vector of the output.
`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.

A matrix of the posterior samples.

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.

Sample_KF_post for more details about sampling from the posterior distribution.

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