Efficient updating of kriging estimates and variances
For sample variance, we are taking the squared difference between data points and the mean, and in the semivariogram we are taking the squared difference between data points separated by distance .
The term is the number of points we have that are separated by the distance .However, the filter yields the exact conditional probability estimate in the special case that all errors are Gaussian.Extensions and generalizations to the method have also been developed, such as the extended Kalman filter and the unscented Kalman filter which work on nonlinear systems.T, residuals ) mu return float( estimation ) X0, X1 = P[:,0].min(), P[:,0].max() Y0, Y1 = P[:,1].min(), P[:,1].max() Z = np.zeros((80,100)) dx, dy = (X1-X0)/100.0, (Y1-Y0)/80.0 for i in range( 80 ): print i, for j in range( 100 ): Z[i,j] = krige( P, model, hs, bw, (dy*j,dx*i), 16 ) cdict = my_cmap = matplotlib.colors. Wiley Online Library requires cookies for authentication and use of other site features; therefore, cookies must be enabled to browse the site.
As such, it is a common sensor fusion and data fusion algorithm.