= Universal kriging
For unknown drift $X\beta$, a full-column-rank <design matrix> $X$, a known <positive-definite matrix> observation <covariance matrix> $\Sigma$, and target drift row $x_0^T$, universal kriging minimizes prediction <variance> subject to $X^Tw=x_0$. Equivalently, with $\widehat\beta=(X^T\Sigma^{-1}X)^{-1}X^T\Sigma^{-1}z$, predict $x_0^T\widehat\beta+c^T\Sigma^{-1}(z-X\widehat\beta)$. The first term is the estimated trend alone; the second is a correlated residual prediction.
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