Created
May 3, 2017 14:04
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""" | |
This is a version of the Sprinkler Bayesian Network example from | |
https://healthyalgorithms.com/2011/11/23/causal-modeling-in-python-bayesian-networks-in-pymc/ | |
translated from pymc to pymc3. | |
""" | |
from pymc3 import Model, Bernoulli, sample, traceplot | |
from pymc3.math import switch | |
from matplotlib.pyplot import savefig | |
Nsamples = 10000 | |
with Model() as sprinkler_model: | |
Rain = Bernoulli('Rain', .2) | |
pSprinkler = switch( Rain, 0.01, 0.4) | |
Sprinkler = Bernoulli('Sprinkler', pSprinkler) | |
pWetGrass = switch( Rain, | |
switch( Sprinkler, 0.99, 0.8 ), | |
# need to apply some smoothing: | |
switch( Sprinkler, .9, 1/Nsamples ) ) | |
WetGrass = Bernoulli('WetGrass', pWetGrass, observed=1 ) | |
trace = sample(Nsamples) | |
traceplot(trace) | |
savefig('trace_{}.png'.format(Nsamples)) |
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