method-multilevel_sampling-qoi_aggregation

Blurb:: 
Aggregation strategy for the QoIs statistics for problems with multiple responses in the MLMC algorithm

Description:: 
In the multilevel method a variance of the \c allocation_target is computed for each of the responses and their levels \f$Y^i_\ell, i = 1,..., R, \ell = 0,..., L \f$. Setting \c qoi_aggregation describes the rule on how to aggregate those variances over multiple response functions. Supported options are \c sum (default) and \c max. For \c sum, the variances are aggregated and a single sample allocation is computed. For \c max, an individual sample allocation for each response using the respective variances over levels is computed and the maximum over all responses for each level is taken (worst case scenario allocation).

<b> Default Behavior </b>
"sum"

Topics::

Examples::
The following method block 
\verbatim
method,
	model_pointer = 'HIERARCH'
        multilevel_sampling
	  pilot_samples = 20 seed = 1237
	  convergence_tolerance = .01
	  allocation_target = mean
      qoi_aggregation = sum
\endverbatim

uses the sum rule to aggregate the variance over the qois.

Theory::
Faq::
See_Also::	