utils for design DOE
- statistical method for generating a near-random values from a multidimentional distribution
- LHS was designed by McKay in 1979
- Latin Square
- There is only one sample in each row and each column
- Algorithm overview
- When sampling a function of N variables
- the range of each variable is devided into M
- M is equally probable intervals
- M sample points are then placed to satisfy the Latin Hypercube requirements
- this force the number of divisions
- M is equal for each variables
- main advantage is "independence"
- this sampling scheme does not require more points for more dimensions
- another is that random sampling can be taken one at a time, remembering which samples were taken so far
- this repository utilize