#Latin hypercube sampling simulation full#

A discrete, Monte Carlo model of epidemics of influenzavirus infections in a human community is used for illustrative purposes. The original Latin Hypercube sampling has been. The sample values are randomly shuffled among different variables.

#Latin hypercube sampling simulation full#

The basic of Latin Hypercube sampling is a full stratification of sampled distribution with a random selection inside each stratum.

In this article, a sensitivity study based on a Latin hypercube (LH) sampling design is compared with a similar study using a full factorial (FF), fixed-point sample. The original Latin Hypercube sampling is developed as a variance reduction technique or as a screening technique. To include more model features and their interactions in a sensitivity study, while limiting computer utilization, various sampling methods have been suggested. Any of the two-dimensional projections is still a Latin hypercube design with the same p15 points (although, for this particular case, the x 1 x 2 projection is the best in terms of space lling). Figure 3(a) illustrates the case of a Latin hypercube design with d3 dimensions and p15 points. Sometimes, one or two parameters are modified at a time in an ad hoc fashion in an attempt to assess sensitivity. Latin hypercube samples are non-collapsing. Traditional methods of sensitivity analysis for systems of differential equations do not apply. However, little is typically known about the sensitivity of such models to changes in the values of the model features. LHS or Latin Hypercube Sampling is a sampling method enabling to better cover the domain of variations of the input variables, thanks to a stratified. Discrete, algorithmic simulation and Monte Carlo methodologies are currently used in population biology, connectionist cognitive modeling, and physics.