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)A matrix of the posterior samples.
a vector where the entries with 1 have observations and entries with 0 have no observation.
a list of matrices to compute the inverse covariance matrix in the dynamic linear model.
a list of matrices defined in the dynamic linear model.
a list of coefficient matrices defined in the dynamic linear model.
a numerical value of the variance of the nugget parameter.
a vector of the output.
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.
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.
tools:::Rd_package_author("FastGaSP")
Maintainer: tools:::Rd_package_maintainer("FastGaSP")
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.