latin hypercube sampling and staratified

Latin hypercube sampling and staratified manuals#

Orthogonal sampling adds the requirement that the entire sample space must be sampled evenly. A Latin hypercube of divisions with variables (i.e., a cube) will have 576 possible combinations. The maximum number of combinations for a Latin Hypercube of divisions and variables (i.e., dimensions) can be computed with the following formula:įor example, a Latin hypercube of divisions with variables (i.e., a square) will have 24 possible combinations. Another advantage is that random samples can be taken one at a time, remembering which samples were taken so far. Also note that this sampling scheme does not require more samples for more dimensions (variables) this independence is one of the main advantages of this sampling scheme. sample points are then placed to satisfy the Latin hypercube requirements note that this forces the number of divisions,, to be equal for each variable. When sampling a function of variables, the range of each variable is divided into equally probable intervals. A Latin hypercube is the generalisation of this concept to an arbitrary number of dimensions, whereby each sample is the only one in each axis-aligned hyperplane containing it. In the context of statistical sampling, a square grid containing sample positions is a Latin square if (and only if) there is only one sample in each row and each column.

Latin hypercube sampling and staratified manuals#

Detailed computer codes and manuals were later published. The technique was first described by McKay in 1979. The sampling method is often applied in uncertainty analysis. Latin hypercube sampling ( LHS) is a statistical method for generating a distribution of plausible collections of parameter values from a multidimensional distribution.