Documentation
/ User's manual in Python
: 
| Chapter III. The Sampler module | ||
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Abstract
This is a description of the Sampler module whose main goal is to produce a design-of-experiments, starting from the description of
the model provided by the user, and that would be used to investigate the sensitivity and/or propagate uncertainty
for the requested analysis.
The source files are in souRCE.git/meTIER/sampler/souRCE and the corresponding namespace is URANIE::Sampler.
Table of Contents
The Sampler module is used to produce design-of-experiments knowing the expected behaviour of the input variables for the problem under consideration. The framework of our approach can be illustrated in the following schematic view:
We will denote as
the studied computational code which, generally, has two types of inputs:
The constant parameters which are gathered in the vector
. They
represent constants.
The uncertain parameters which are gathered in the vector
It shall be noticed that these parameters are supposed to be uncertain either because of a lack of knowledge on their actual value or because of their intrinsic random nature.
The result of the code
for a given set of parameters 
gives the vector 
which contains all the output variables of the analysis.
Different methods exist to obtain a design-of-experiments from uncertain parameters which can be classified into two categories:
stochastic methods (see Section III.2). These methods consist in using a random number generator to produce new samples. This is also called Monte-Carlo.
deterministic methods (see Section III.4). Two distinct calls with the same parameters will always give the same point in a design-of-experiments. Some of these methods (those discussed below) are sequences which are sometimes called quasi-Monte Carlo (qMC).
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| II.6. Combining these aspects: performing PCA |  | III.2. The Stochastic methods |