.. _method-bayes_calibration-queso-emulator-ml_pce-allocation_control:

""""""""""""""""""
allocation_control
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Sample allocation approach for multilevel expansions


.. toctree::
   :hidden:
   :maxdepth: 1

   method-bayes_calibration-queso-emulator-ml_pce-allocation_control-estimator_variance
   method-bayes_calibration-queso-emulator-ml_pce-allocation_control-rip_sampling


**Specification**

- *Alias:* None

- *Arguments:* None


**Child Keywords:**

+-------------------------+--------------------+------------------------+-----------------------------------------------+
| Required/Optional       | Description of     | Dakota Keyword         | Dakota Keyword Description                    |
|                         | Group              |                        |                                               |
+=========================+====================+========================+===============================================+
| Required (Choose One)   | Multilevel Sample  | `estimator_variance`__ | Variance of mean estimator within multilevel  |
|                         | Allocation Control |                        | polynomial chaos                              |
|                         |                    +------------------------+-----------------------------------------------+
|                         |                    | `rip_sampling`__       | Sample allocation based on restricted         |
|                         |                    |                        | isometry property (RIP) within multilevel     |
|                         |                    |                        | polynomial chaos                              |
+-------------------------+--------------------+------------------------+-----------------------------------------------+

.. __: method-bayes_calibration-queso-emulator-ml_pce-allocation_control-estimator_variance.html
__ method-bayes_calibration-queso-emulator-ml_pce-allocation_control-rip_sampling.html



**Description**


Multilevel expansions, including regression-based polynomial chaos expansion
(PCE) and function train (FT) expansions, require a sample allocation strategy.
Three options are currently available:


- allocation based on assuming a convergence rate for the estimator variance (for regression PCE)
- restricted isometry property (RIP) sampling (for regression PCE via compressed sensing)
- rank sampling (for FT)

*Default Behavior*

Current defaults, when ``allocation_control`` is not specified, are
estimator variance for PCE and rank sampling for FT.


