These samples are drawn from arbitrary and stratigraphic levels and features such as hearths.
Using this method, multiple bootstrap samples are drawn from the original data set.
The samples are drawn from a uni-variate Gaussian distribution, where all thawed parameters are treated independently.
In the image below at left, samples are drawn from a cosine-weighted distribution centered about the normal of the surface being shaded (see lights/infinite.cpp).cpp
More commonly used methods, refinements of this basic idea, are stratified sampling (in which the population is divided into classes and simple random samples are drawn from each class), cluster sampling (in which the unit of the sample is a group, such as a household), and systematic sampling (samples taken by any system other than random choice, such as every 10th name on a list).
where the random samples are drawn from distribution.
Let us assume that a large number of samples are drawn from the posterior distribution.
The samples are drawn from a high-dimensional space (R^mathrm{D}).
In particular, very few samples are drawn from the finest grids (with the highest computational cost) and very large number of samples are drawn from the coarsest grids (with very low computational cost).
The samples are drawn from subpopulations such as specific immigrant groups, optimizing the comparison of such groups.
The samples are drawn from the Netherlands Municipal Basic Register which includes basic information of all citizens in the Netherlands.
