Assuming your two variables, x 1 and x 2 are independent, you follow the one-dimensional method to come up with one dimensional samples for x 1 and x 2 separately. Two-dimensional Latin hypercube sampling is not much more complicated and is usually performed with software. In each interval you would randomly select one point, giving you 100 different points. The second data point would be from the interval (k/100, 2k/100), your third from (2k/100, 3k/100), and so on. If your distribution starts at 0 and ends with k, your first data point would be selected from the interval between (0,k/100). First, divide the cdf into 100 equal intervals. One-dimensional Latin hypercube sampling involves dividing your cumulative density function (cdf) into n equal partitions and then choosing a random data point in each partition.Īs a simple example, let’s say you needed a random sample with 100 data points. The Method Behind Latin Hypercube Sampling
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