Create a Dense Functional Data sample for a Gaussian process
For a Gaussian process, create a dense functional data sample of size n over a [0,1] support.
MakeGPFunctionalData( n, M = 100, mu = rep(0, M), K = 2, lambda = rep(1, K), sigma = 0, basisType = "cos" )
n |
number of samples to generate |
M |
number of equidistant readings per sample (default: 100) |
mu |
vector of size M specifying the mean (default: rep(0,M)) |
K |
scalar specifying the number of basis to be used (default: 2) |
lambda |
vector of size K specifying the variance of each components (default: rep(1,K)) |
sigma |
The standard deviation of the Gaussian noise added to each observation points. |
basisType |
string specifying the basis type used; possible options are: 'sin', 'cos' and 'fourier' (default: 'cos') (See code of 'CreateBasis' for implementation details.) |
Y: X(t_j), Yn: noisy observations
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