Latin Hypercube Sampling (LHS) is a statistical method used to generate a sample of plausible input values from a multidimensional distribution. This technique ensures that the sample captures the entire range of possible outcomes, making it particularly useful in scenarios where simulations are required across multiple parameters. By stratifying the input distributions, LHS allows for efficient exploration of parameter space, which is essential for accurate multi-scale modeling approaches.
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