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Answer: Fewer scenarios are required
The Latin Hypercube sampling technique is a stratified sampling method that divides the probability distribution into equal probability intervals. Compared to traditional Monte Carlo simulation, Latin Hypercube sampling requires fewer scenarios to achieve the same level of accuracy because it ensures better coverage of the input space by sampling from each stratum. This makes it more efficient than simple random sampling used in traditional Monte Carlo methods.
Author: Nikitesh Somanthe
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One of the advantages of the Latin Hypercube sampling technique over the traditional Monte Carlo simulation is that:
A
It's easier to obtain clustered observations
B
There’s no need to represent each stratum of data
C
We do not need to assume the probability distribution of the inputs
D
Fewer scenarios are required