Aggregation of various sources of information, expert elicitation, or evidence

Installation

Install the pyuncertainnumber library from PyPI.

pip install pyuncertainnumber

Important

A virtual enviroment is recommended for installation.

Follow the instructions for additional details to install pyuncertainnumber.

from pyuncertainnumber import UN
import pyuncertainnumber as pun
import numpy as np
import matplotlib.pyplot as plt

from pyuncertainnumber import stochastic_mixture, envelope
from pyuncertainnumber.pba.dss import plot_DS_structure_with_labels

expert elicitation is represented as probability distribution

a_d = pun.D('gaussian', (-5, 2))
b_d = pun.D('normal', (1.5, 3.))
c_d = pun.D('uniform', (1, 12))
d_d = pun.D('gaussian', (10, 1.5))
fixed_value=0
p_env = envelope(a_d, b_d, c_d, d_d)

fig, ax = plt.subplots(figsize=(4, 3))
a_d.plot(ax=ax, label='expert a', color='green', ls='--', zorder=100)
b_d.plot(ax=ax, color='salmon', zorder=50, ls='--', label='expert b')
c_d.plot(ax=ax, label='expert c', color='purple', ls='--', zorder=100)
d_d.plot(ax=ax, label='expert d', color='blue', ls='--', zorder=100)
ax.vlines(fixed_value, ymin=0, ymax=1, color='black', linestyle='dashdot', label='expert e')
p_env.plot(ax=ax,  bound_colors=['lightgray', 'lightgray'], fill_color='lightgray', label='envelope p-box')
ax.set_title("envelope aggregation of expert elicitations")
ax.legend(loc="center left", bbox_to_anchor=(1, 0.5))
plt.show()

expert opinion is represented as intervals

a = pun.I(1,5)
b = pun.I(3,6)
c = pun.I(2,3)
d = pun.I(2,9)

dss = stochastic_mixture(a,b,c,d)

fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(8, 3))                          
ax0 = plot_DS_structure_with_labels([a,b,c,d], masses=[0.25, 0.25, 0.25, 0.25], offset=1.5, ax=ax0)
dss.plot(ax=ax1)
plt.show()