This function samples the piror process using a dynamic liner model.
Sample_KF(GG,W,C0,VV,kernel_type,sample_type)a list of matrices defined in the dynamic linear model.
a list of coefficient matrices defined in the dynamic linear model.
the covariance matrix of the stationary distribution defined in the dynamic linear model.
a numerical value of the variance of the nugget parameter.
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.
A matrix of the samples.
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.