Sample_KF_post {FastGaSP}R Documentation

Sample the posterior distribution of the process using the backward smoothing algorithm

Description

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

Usage

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

Arguments

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.

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.

Value

A matrix of the posterior samples.

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.

See Also

Sample_KF_post for more details about sampling from the posterior distribution.


[Package FastGaSP version 0.6.1 Index]