This algorithm relies on the following steps for drawing N samples from a hypercube-shaped of dimension p.:ДЎ) Divide each dimension of the space in N equiprobabilistic bins. A quick and popular way to generate a sample that covers the space fairly well is latin hypercube sampling (LHS McKay et al., 1979). The difficulty though, is how we define вЂњhow wellвЂќ it practice, and the implications that has. Intuitively, the first criterion for a good sample is how well it covers the space from which to sample. Then it will show some handy visualization tools for quickly testing and visualizing a sample.

It will first look at what makes a good sample using some examples from a sampling technique called latin hypercube sampling. Likewise, the method of Morris ( Morris, 1991), less computationally demanding than SobolвЂ™s ( Herman et al., 2013) and used for screening (i.e., understanding which are the inputs that most influence outputs), relies on specific sampling techniques ( Morris, 1991 Campolongo et al., 2007).Д«ut what makes a good sample, and how can we understand the strengths and weaknesses of the sampling techniques (and also of the associated sensitivity techniques we are using) through quick visualization of some associated metrics? The popular method of SobolвЂ™ ( SobolвЂ™, 2001) relies on tailor-made sampling techniques that have been perfected through time (e.g., Joe and Kuo, 2008 Saltelli et al., 2010). This is the case for instance, for sensitivity analysis (i.e., the analysis of model output sensitivity to values of the input variables). State sampling is a necessary step for any computational experiment, and the way sampling is carried out will influence the experimentвЂ™s results.

Progressive latin hypercube sampling