Estimate a probability with Latin Hypercube SamplingΒΆ

In this example we show how to use the LHS algorithm to estimate the probability of an event. We consider the axial stressed beam example.

from openturns.usecases import stressed_beam
import openturns as ot


We load the model from the usecases module :

sm = stressed_beam.AxialStressedBeam()

We get the input parameter distribution :

distribution = sm.distribution

and get the model :

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 LHS algorithm.

algo = ot.LHS(event)

Retrieve results.

result = algo.getResult()
probability = result.getProbabilityEstimate()
print("Pf=", probability)
Pf= 0.029342988609791055

Total running time of the script: ( 0 minutes 0.038 seconds)