About the Sampling Techniques

The Simple Random Sampling technique generates sample points by generating $N$ uniformly distributed random numbers between 0 and 1 for each random variable and obtaining corresponding values from each random variable distribution.

The Descriptive Sampling technique is a variance reduction technique that generates sample points by dividing each random variable distribution into $N$ intervals of equal probability and randomly combining samples from these intervals for each random variable to produce design points.

The Sobol Sampling technique is a variance reduction technique that is a sub-random sequence of numbers that are more uniformly distributed than both the simple random sampling technique and the descriptive sampling technique. The Sobol sampling technique generates numbers as binary fractions of appropriate length from a set of special binary fractions.

Monte Carlo Simulation methods are considered the most accurate means of estimating the probabilistic properties of uncertain system responses resulting from uncertain inputs. To implement a Monte Carlo simulation, a defined number of system simulations to be analyzed are generated by sampling values of random variables (uncertain inputs), following the probabilistic distributions and associated properties defined for each. Sampling techniques in Isight are implemented as plug-ins. As such, they are extendable by creating new plug-ins for new sampling techniques. For more information, see Plug-In Development in the Isight Development Guide.