.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_reliability_sensitivity/reliability/plot_estimate_probability_monte_carlo.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_reliability_sensitivity_reliability_plot_estimate_probability_monte_carlo.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_monte_carlo.py:


Estimate a probability with Monte Carlo
=======================================

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In this example we estimate a probability by means of a simulation algorithm, the Monte-Carlo algorithm. To do this, we need the classes `MonteCarloExperiment` and `ProbabilitySimulationAlgorithm`.
We consider the :ref:`axial stressed beam <use-case-stressed-beam>` example.

.. GENERATED FROM PYTHON SOURCE LINES 11-18

.. code-block:: default

    from __future__ import print_function
    from openturns.usecases import stressed_beam as stressed_beam
    import openturns as ot
    import openturns.viewer as viewer
    from matplotlib import pylab as plt
    ot.Log.Show(ot.Log.NONE)








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We load the model from the usecases module :

.. GENERATED FROM PYTHON SOURCE LINES 20-22

.. code-block:: default

    sm = stressed_beam.AxialStressedBeam()








.. GENERATED FROM PYTHON SOURCE LINES 23-24

We get the joint distribution of the parameters.

.. GENERATED FROM PYTHON SOURCE LINES 24-26

.. code-block:: default

    distribution = sm.distribution








.. GENERATED FROM PYTHON SOURCE LINES 27-28

The model is also stored in the data class :

.. GENERATED FROM PYTHON SOURCE LINES 28-30

.. code-block:: default

    model = sm.model








.. GENERATED FROM PYTHON SOURCE LINES 31-32

We create the event whose probability we want to estimate.

.. GENERATED FROM PYTHON SOURCE LINES 34-38

.. code-block:: default

    vect = ot.RandomVector(distribution)
    G = ot.CompositeRandomVector(model, vect)
    event = ot.ThresholdEvent(G, ot.Less(), 0.0)








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Create a Monte Carlo algorithm.

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.. code-block:: default

    experiment = ot.MonteCarloExperiment()
    algo = ot.ProbabilitySimulationAlgorithm(event, experiment)
    algo.setMaximumCoefficientOfVariation(0.05)
    algo.setMaximumOuterSampling(int(1e5))
    algo.run()








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Retrieve results.

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.. code-block:: default

    result = algo.getResult()
    probability = result.getProbabilityEstimate()
    print('Pf=', probability)




.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Pf= 0.02936292270531395





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.061 seconds)


.. _sphx_glr_download_auto_reliability_sensitivity_reliability_plot_estimate_probability_monte_carlo.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_estimate_probability_monte_carlo.py <plot_estimate_probability_monte_carlo.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_estimate_probability_monte_carlo.ipynb <plot_estimate_probability_monte_carlo.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_