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 strataRefinedStratifiedSampling
: Class is a sequential sampling procedure that adaptively refines the stratification of the parameter space to add samplesSimplexSampling
: 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) frameworkThetaCriterionPCE
: 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 computeImportanceSampling
: 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