PGM course: compute P(L1)

reasoning_l1.py

import numpy as np

P_D = np.array([0.6, 0.4])
P_I = np.array([0.7, 0.3])

P_G_ID = np.array([
    [[0.3, 0.05],
     [0.9, 0.5]],

    [[0.4, 0.25],
     [0.08, 0.3]],

    [[0.3, 0.7],
     [0.02, 0.2]]
    ])

P_L_G = np.array(
    [[0.1, 0.4, 0.99],
     [0.9, 0.6, 0.01]]
    )

P_ID = P_I[..., None] * P_D
P_GID = P_G_ID * P_ID

P_G = np.sum(P_GID, axis=(-1, -2))
P_LG = P_L_G * P_G

# Marginalize the joint distribution over G
# where L=1.
#
# P(L1) ~= 0.502336

print P_LG[1, :].sum()