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Why use latin hypercube sampling?
Latin Hypercube Sampling (LHS) is a way of generating random samples of parameter values. It is widely used in Monte Carlo simulation, because it can drastically reduce the number of runs necessary to achieve a reasonably accurate result.
What is Latin hypercube sampling and what is its advantage over Monte Carlo simulation?
Monte Carlo (MC) simulation generates a random sample of N points for each uncertain input variable of a model. It selects each point independently from the probability distribution for that input variable. Latin Hypercube sampling (LHS) aims to spread the sample points more evenly across all possible values [7].
How does latin hypercube sampling work?
Latin Hypercube sampling (LHS) aims to spread the sample points more evenly across all possible values [7]. It partitions each input distribution into N intervals of equal probability, and selects one sample from each interval.
What is conditioned Latin hypercube sampling?
Conditioned Latin Hypercube Sampling (cLHS) is a type of stratified random sampling that accurately represents the variability of environmental covariates in feature space. As the smallest possible sample is important for efficient field work, what is the optimal sample size for digital soil mapping?
How is Latin hypercube sampling different from Monte Carlo sampling?
Monte Carlo Sampling (MCS) and Latin Hypercube Sampling (LHS) are two methods of sampling from a given probability distribution. In MCS we obtain a sample in a purely random fashion whereas in LHS we obtain a pseudo-random sample, that is a sample that mimics a random structure.
What’s the difference between LHS and Latin hypercube sampling?
In MCS we obtain a sample in a purely random fashion whereas in LHS we obtain a pseudo-random sample, that is a sample that mimics a random structure. In order to give a rough idea, MC simulation can be compared to simple random sampling whereas Latin Hypercube Sampling can be compared to stratified sampling.
Is there a code for a Latin hypercube simulation?
The following is a code for a Latin Hypercube Simulation. It is similar to the one for MCS. However here the interval [0,1] is divided into portions and a number is sampled randomly from each interval.
Which is better LHS or Monte Carlo sampling?
One can say that a sample of size 400 using LHS is a more representative sample than a Monte Carlo sample of size 6,000. The advantage of LHS over MCS is that you need a much lower sample size in order to obtain some particular precision.