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However, as the example in Figure 21.20 shows, the Latin Hypercube design might not provide you with optimal space filling properties. This example is a two-factor 16 run Latin Hypercube with factor level settings set between -1 and 1. The plot of the two factors shows that this design has regions that are missing coverage. In particular, there is poor coverage for X1 near 0 and X2 near -1.
Figure 21.20 Two-factor Latin Hypercube Design
Figure 21.21 Two-Factor Maximum Entropy Design
Technical Maximum Entropy designs maximize the Shannon information (Shewry and Wynn (1987)) of an experiment, assuming that the data come from a normal (m, s2 R) distribution, where
is the correlation of response values at two different design points, xi and xj. Computationally, these designs maximize |R|, the determinant of the correlation matrix of the sample. If xi and xj are far apart, then Rij approaches zero. If xi and xj are close together, then Rij is near one.

Help created on 3/19/2020