Create a maximum entropy statistics distribution

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

from __future__ import print_function
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 distribution

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)

Out:

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	0.6440287	1.086573
1	0.7141691	0.9588674
2	0.4362711	0.6576377
3	0.5940418	0.6238439
4	0.5273163	0.5399325
5	0.3097874	0.742308
6	0.6399305	0.7142689
7	0.8138742	0.8253311
8	0.5889975	0.7029539
9	0.4969304	0.5786498

draw PDF

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