.. _method-bayes_calibration-wasabi-emulator-pce-orthogonal_least_interpolation:

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orthogonal_least_interpolation
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Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.


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

   method-bayes_calibration-wasabi-emulator-pce-orthogonal_least_interpolation-collocation_points
   method-bayes_calibration-wasabi-emulator-pce-orthogonal_least_interpolation-tensor_grid
   method-bayes_calibration-wasabi-emulator-pce-orthogonal_least_interpolation-reuse_points
   method-bayes_calibration-wasabi-emulator-pce-orthogonal_least_interpolation-import_build_points_file
   method-bayes_calibration-wasabi-emulator-pce-orthogonal_least_interpolation-posterior_adaptive


**Specification**

- *Alias:* least_interpolation oli 

- *Arguments:* None


**Child Keywords:**

+-------------------------+--------------------+------------------------------+-----------------------------------------------+
| Required/Optional       | Description of     | Dakota Keyword               | Dakota Keyword Description                    |
|                         | Group              |                              |                                               |
+=========================+====================+==============================+===============================================+
| Required                                     | `collocation_points`__       | Number of collocation points used to estimate |
|                                              |                              | expansion coefficients                        |
+----------------------------------------------+------------------------------+-----------------------------------------------+
| Optional                                     | `tensor_grid`__              | Use sub-sampled tensor-product quadrature     |
|                                              |                              | points to build a polynomial chaos expansion. |
+----------------------------------------------+------------------------------+-----------------------------------------------+
| Optional                                     | `reuse_points`__             | This describes the behavior of reuse of       |
|                                              |                              | points in constructing polynomial chaos       |
|                                              |                              | expansion models.                             |
+----------------------------------------------+------------------------------+-----------------------------------------------+
| Optional                                     | `import_build_points_file`__ | File containing points you wish to use to     |
|                                              |                              | build a surrogate                             |
+----------------------------------------------+------------------------------+-----------------------------------------------+
| Optional                                     | `posterior_adaptive`__       | Adapt emulator model to increase accuracy in  |
|                                              |                              | high posterior probability regions            |
+----------------------------------------------+------------------------------+-----------------------------------------------+

.. __: method-bayes_calibration-wasabi-emulator-pce-orthogonal_least_interpolation-collocation_points.html
__ method-bayes_calibration-wasabi-emulator-pce-orthogonal_least_interpolation-tensor_grid.html
__ method-bayes_calibration-wasabi-emulator-pce-orthogonal_least_interpolation-reuse_points.html
__ method-bayes_calibration-wasabi-emulator-pce-orthogonal_least_interpolation-import_build_points_file.html
__ method-bayes_calibration-wasabi-emulator-pce-orthogonal_least_interpolation-posterior_adaptive.html



**Description**


Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation. Unlike the other regression methods ``expansion_order`` cannot be set. OLI will produce the lowest degree polynomial that interpolates the data


