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Cramér-von Mises Sensitivity indices
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
These examples serve as a guide for using the Cramér-von Mises sensitivity module. They have been taken from various papers to enable validation of the implementation and have been referenced accordingly.

1. **Exponential function**

    For the Exponential model, analytical Cramér-von Mises indices are available :cite:`CVM`.

2. **Sobol function**

    The Cramér-von Mises indices are computed using the Pick and Freeze approach :cite:`CVM`. These model evaluations can be used to estimate the Sobol indices as well. We demonstrate this using the Sobol function.



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  :ref:`sphx_glr_auto_examples_sensitivity_cramer_von_mises_cvm_sobol_func.py`

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    <div class="sphx-glr-thumbcontainer" tooltip="The exponential function was used in [1]_ to demonstrate the  Cramér-von Mises indices.">

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  :ref:`sphx_glr_auto_examples_sensitivity_cramer_von_mises_cvm_exponential.py`

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   /auto_examples/sensitivity/cramer_von_mises/cvm_sobol_func
   /auto_examples/sensitivity/cramer_von_mises/cvm_exponential


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      :download:`Download all examples in Python source code: cramer_von_mises_python.zip </auto_examples/sensitivity/cramer_von_mises/cramer_von_mises_python.zip>`

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      :download:`Download all examples in Jupyter notebooks: cramer_von_mises_jupyter.zip </auto_examples/sensitivity/cramer_von_mises/cramer_von_mises_jupyter.zip>`


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