Due to its high accuracy on small scales, the FORGE matter power spectrum emulator is well suited for weak lensing analysis and can play a key tool in constraining $f(R)$ gravity using current and future observational data. We have also checked the power spectrum emulator against simulations which are not part of our training set and found excellent agreement. The total of 200 simulations explore the cosmological parameter space around the Planck(2018) cosmology with a Latin hypercube, for 50 combinations of $\bar$. First, you just make 1-D LHS for regular grid. And then, combine the 1-D LHS to make 2-D LHS.
![matlab latin hypercube sampling code matlab latin hypercube sampling code](https://datasciencegenie.com/wp-content/uploads/2020/05/ContourBivariate.png)
LHS for 3-D can also be created using the same method (by combining 2-D LHS). Generally, the number of possible LHS is N x ( (M-1)) (M-1). See also the example on an integer space sphxglrautoexamplesinitialsamplingmethodinteger. Then these points can be spread out in such a way that each dimension is explored.
MATLAB LATIN HYPERCUBE SAMPLING CODE CODE
We present a large suite of cosmological simulations, the FORGE (F-of-R Gravity Emulator) simulation suite, which is designed to build accurate emulators for cosmological observables in galaxy clustering, weak gravitational lensing and galaxy clusters, for the $f(R)$ gravity model. MATLAB LATIN HYPERCUBE SAMPLING CODE CODE The following code shows LHS for 3-D and 10 divisions. Sampling methods as Latin hypercube, Sobol, Halton and Hammersly take advantage of the fact that we know beforehand how many random points we want to sample. Latin-hypercube designs can be created using the following simple syntax: n: an integer that designates the number of factors (required) samples: an integer that designates the number of sample points to generate for each factor (default: n) maximin or m: maximize the minimum distance between points, but place the point in a. For example: X lhsdesign (10000,1) Y betainv (X,5,2) histogram (Y) Share. Let’s assume that we’d like to perform LHS for 10 data points in the 1-dimension data space. You can use lhsdesign to get a set of uniformly distributed numbers, then using Inverse transform sampling method you convert them to beta distribution.
![matlab latin hypercube sampling code matlab latin hypercube sampling code](https://www.mathworks.com/matlabcentral/mlc-downloads/downloads/submissions/49675/versions/1/screenshot.jpg)
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.