Note
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Estimate a probability with Monte CarloΒΆ
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 axial stressed beam example.
from openturns.usecases import stressed_beam
import openturns as ot
ot.Log.Show(ot.Log.NONE)
We load the model from the usecases module :
sm = stressed_beam.AxialStressedBeam()
We get the joint distribution of the parameters.
distribution = sm.distribution
The model is also stored in the data class :
model = sm.model
We create the event whose probability we want to estimate.
vect = ot.RandomVector(distribution)
G = ot.CompositeRandomVector(model, vect)
event = ot.ThresholdEvent(G, ot.Less(), 0.0)
Create a Monte Carlo algorithm.
experiment = ot.MonteCarloExperiment()
algo = ot.ProbabilitySimulationAlgorithm(event, experiment)
algo.setMaximumCoefficientOfVariation(0.05)
algo.setMaximumOuterSampling(int(1e5))
algo.run()
Retrieve results.
result = algo.getResult()
probability = result.getProbabilityEstimate()
print("Pf=", probability)
Pf= 0.030314868349089773