Sampling

This module contains functionality for all the sampling methods supported in UQpy.

The module currently contains the following classes:

• MonteCarloSampling: Class generating random samples from a specified probability distribution(s).

• LatinHypercubeSampling: Class generating random samples from a specified probability distribution(s) using Latin hypercube sampling.

• TrueStratifiedSampling: Class is a variance reduction technique that divides the parameter space into a set of disjoint and space-filling strata

• RefinedStratifiedSampling: Class is a sequential sampling procedure that adaptively refines the stratification of the parameter space to add samples

• SimplexSampling: Class generating uniformly distributed samples inside a simplex.

• AdaptiveKriging: Class generating samples adaptively using a specified Kriging-based learning function in a general Adaptive Kriging-Monte Carlo Sampling (AKMCS) framework

• ThetaCriterionPCE: Active learning for polynomial chaos expansion using Theta criterion balancing between exploration and exploitation.

• MCMC: The goal of Markov Chain Monte Carlo is to draw samples from some probability distribution which is hard to compute

• ImportanceSampling: Importance sampling (IS) is based on the idea of sampling from an alternate distribution and reweighing the samples to be representative of the target distribution