Sample_KF_post | R Documentation |
This function samples the posterior distribution of the process using the backward smoothing algorithm.
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. |
VV |
a numerical value of the variance of the nugget parameter. |
output |
a vector of the output. |
kernel_type |
a |
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], Xinyi Fang [aut], Yizi Lin [aut]
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.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.