Create a maximum entropy statistics distribution

In this example we are going to build maximum entropy statistics distribution, which yields ordered realizations:

import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt

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

Create a collection of distributions

coll = [ot.Beta(1.5, 1.7, 0.0, 1.0), ot.Beta(2.0, 2.3, 0.5, 1.2)]

Create the distribution

distribution = ot.MaximumEntropyOrderStatisticsDistribution(coll)
print(distribution)

MaximumEntropyOrderStatisticsDistribution(collection = [Beta(alpha = 1.5, beta = 1.7, a = 0, b = 1),Beta(alpha = 2, beta = 2.3, a = 0.5, b = 1.2)])

Draw a sample (ordered!)

distribution.getSample(10)

	X0	X1
0	0.8832486	0.8957517
1	0.5836542	0.6722579
2	0.3265067	0.9604826
3	0.8183162	1.119319
4	0.5247459	0.6703216
5	0.8002262	0.8102919
6	0.8286967	0.9409432
7	0.6790498	0.9014674
8	0.1614631	1.049137
9	0.1646297	0.5626579

Draw PDF

graph = distribution.drawPDF()
view = viewer.View(graph)
plt.show()