# 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

ot.Log.Show(ot.Log.NONE)
```

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)
algo.setMaximumCoefficientOfVariation(0.05)
algo.setMaximumOuterSampling(int(1e5))
algo.run()
```

Retrieve results.

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