# Create a mixture of PDFs¶

In this example we are going to build a distribution whose PDF is defined by a linear combination of probability density functions:

The weigths are automatically normalized.

It is also possible to create a mixture of copulas.

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from __future__ import print_function
import openturns as ot

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# create a collection of distribution and the associated weights
distributions = [ot.Triangular(1.0, 2.0, 4.0), ot.Normal(-1.0, 1.0), ot.Uniform(5.0, 6.0)]
weights = [0.4, 1.0, 0.2]

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# create the mixture
distribution = ot.Mixture(distributions, weights)
print(distribution)

Mixture((w = 0.25, d = Triangular(a = 1, m = 2, b = 4)), (w = 0.625, d = Normal(mu = -1, sigma = 1)), (w = 0.125, d = Uniform(a = 5, b = 6)))

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# draw PDF
distribution.drawPDF()

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[16]:

# define a list of copulas and the associated weights
copulas = [ot.GumbelCopula(4.5), ot.ClaytonCopula(2.3)]
weights = [0.2, 0.8]

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# create a mixture of copulas
distribution = ot.Mixture(copulas, weights)
print(distribution)

Mixture((w = 0.2, d = GumbelCopula(theta = 4.5)), (w = 0.8, d = ClaytonCopula(theta = 2.3)))

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# draw PDF
distribution.drawPDF()

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