kriging: Apply the gaussian conditioning theorem to derive a posterior...

In saidqasmi/KCC: Kriging for Climate Change (KCC)

Apply the gaussian conditioning theorem to derive a posterior distribution from a prior and observations

Description

Apply the gaussian conditioning theorem to derive a posterior distribution from a prior and observations

Usage

kriging(x, SX, y, SY, H)

Arguments

x	a vector of time series corresponding to the mean of the prior
SX	the covariance matrix corresponding to x. The number of lines and columns must be equal to the length of x.
y	a vector of time series corresponding to the observations
SY	the covariance matrix corresponding to y. The number of lines and columns must be equal to the length of y.
H	a matrix corresponding to an observation operator. The number of lines (columns) must be equal to the length of y(x).

Value

a list of two lists. The first list mean contains the mean of the posterior. The second list var contains the associated covariance matrix.

saidqasmi/KCC documentation built on July 8, 2022, 6:02 a.m.