.. 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 :ref:`Go to the end ` 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 ======================================= .. GENERATED FROM PYTHON SOURCE LINES 6-9 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 ` example. .. GENERATED FROM PYTHON SOURCE LINES 12-17 .. code-block:: Python from openturns.usecases import stressed_beam import openturns as ot ot.Log.Show(ot.Log.NONE) .. GENERATED FROM PYTHON SOURCE LINES 18-19 We load the model from the usecases module : .. GENERATED FROM PYTHON SOURCE LINES 19-21 .. code-block:: Python sm = stressed_beam.AxialStressedBeam() .. GENERATED FROM PYTHON SOURCE LINES 22-23 We get the joint distribution of the parameters. .. GENERATED FROM PYTHON SOURCE LINES 23-25 .. code-block:: Python distribution = sm.distribution .. GENERATED FROM PYTHON SOURCE LINES 26-27 The model is also stored in the data class : .. GENERATED FROM PYTHON SOURCE LINES 27-29 .. code-block:: Python model = sm.model .. GENERATED FROM PYTHON SOURCE LINES 30-31 We create the event whose probability we want to estimate. .. GENERATED FROM PYTHON SOURCE LINES 33-37 .. code-block:: Python vect = ot.RandomVector(distribution) G = ot.CompositeRandomVector(model, vect) event = ot.ThresholdEvent(G, ot.Less(), 0.0) .. GENERATED FROM PYTHON SOURCE LINES 38-39 Create a Monte Carlo algorithm. .. GENERATED FROM PYTHON SOURCE LINES 41-47 .. code-block:: Python experiment = ot.MonteCarloExperiment() algo = ot.ProbabilitySimulationAlgorithm(event, experiment) algo.setMaximumCoefficientOfVariation(0.05) algo.setMaximumOuterSampling(int(1e5)) algo.run() .. GENERATED FROM PYTHON SOURCE LINES 48-49 Retrieve results. .. GENERATED FROM PYTHON SOURCE LINES 51-54 .. code-block:: Python result = algo.getResult() probability = result.getProbabilityEstimate() print("Pf=", probability) .. rst-class:: sphx-glr-script-out .. code-block:: none Pf= 0.030314868349089773 .. _sphx_glr_download_auto_reliability_sensitivity_reliability_plot_estimate_probability_monte_carlo.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_estimate_probability_monte_carlo.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_estimate_probability_monte_carlo.py `