Matlab latin hypercube sampling lognormal

Matlab latin hypercube sampling lognormal code#

The value where is the inverse of the cumulative distribution function, is our first sample point, which we call. We generate a random number between 0 and 1 and call it (that is is uniformly-distributed on the interval ). We would like to generate a random sample of values from this distribution. Suppose that we have a random variable with a probability density function and cumulative distribution function. The two sampling methods are then extended to and demonstrated on bivariate cases, for which the rate of convergence is also analysed. The methods are compared with each other in terms of convergence.

Matlab latin hypercube sampling lognormal code#

Both methods are explained and the R code for generating samples is provided for each method.

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.