setup_oakley_function¶

pyapprox.benchmarks.benchmarks.setup_oakley_function()[source]¶

Setup the Oakely function benchmark

\[f(z) = a_1^Tz + a_2^T\sin(z) + a_3^T\cos(z) + z^TMz\]

where \(z\) consists of 15 I.I.D. standard Normal variables and the data \(a_1,a_2,a_3\) and \(M\) are defined in the function pyapprox.benchmarks.sensitivity_benchmarks.get_oakley_function_data().

>>> from pyapprox.benchmarks.benchmarks import setup_benchmark
>>> benchmark=setup_benchmark('oakley')
>>> print(benchmark.keys())
dict_keys(['fun', 'variable', 'mean', 'variance', 'main_effects'])

Returns

benchmarkpya.Benchmark: Object containing the benchmark attributes

References

OakelyOJRSB2004: Oakley, J.E. and O’Hagan, A. (2004), Probabilistic sensitivity analysis of complex models: a Bayesian approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66: 751-769.