![]() We will see something similar when simulating using MCS and LHS. The advantage of stratified sampling over simple random sampling is that even though it is not purely random, it requires a smaller sample size to attain the same precision of the simple random sampling. ![]() In random sampling the 20 people are chosen randomly (without the use of any structured method) and in stratified sampling, 4 people are chosen randomly from each of the 5 districts. Suppose we want to pick 20 people from a city which has 5 districts. 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. ![]() 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. Monte Carlo Sampling (MCS) and Latin Hypercube Sampling (LHS) are two methods of sampling from a given probability distribution. ![]()
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