BorgonovoSensitivity

class BorgonovoSensitivity

Class to define a Borgonovo sensitivity benchmark problem.

Methods

getFirstOrderIndices()

Returns the first order Sobol' sensitivity indices.

getFunction()

Returns the function.

getInputDistribution()

Returns the input distribution.

getName()

Returns the name of the problem.

getTotalOrderIndices()

Returns the total order Sobol' sensitivity indices.
__init__()

Create a Borgonovo sensitivity problem.

The function is defined by the equation:

g(x) = x1 * x2 + x3

where x1, x2, x3 ~ U(0, 1).

The input random variables are independent.

Notes

The dimension and parameters of this problem cannot be changed. The Sobol’ sensitivity indices are exact.

The model was first introduced in (Borgonovo, 2017).

References

  • Borgonovo, Emanuele. “Sensitivity analysis.” An Introduction for the Management Scientist. International Series in Operations Research and Management Science. Cham, Switzerland: Springer (2017). p.18 and p.157.

Examples

>>> import otbenchmark as otb
>>> problem = otb.BorgonovoSensitivity()
getFirstOrderIndices()

Returns the first order Sobol’ sensitivity indices.

Parameters:
None.
Returns:
firstOrderIndices: ot.Point

The first order sensitivity indices.

getFunction()

Returns the function.

Parameters:
None.
Returns:
function: ot.Function

The function.

getInputDistribution()

Returns the input distribution.

Parameters:
None.
Returns:
distribution: ot.Distribution

The distribution.

getName()

Returns the name of the problem.

Parameters:
None.
Returns:
name: str

The name.

getTotalOrderIndices()

Returns the total order Sobol’ sensitivity indices.

Parameters:
None.
Returns:
totalOrderIndices: ot.Point

The total order sensitivity indices.