[0.1, 0.2, 1, 1, 0.8, 0.9, 1, 1]
], #p(~G)
evidence=[
'BrokeElectionLaw', 'PoliticallyMotivatedProsecutor', 'Indicted'
],
evidence_card=[2, 2, 2])
cpd_j = TabularCPD(variable='Jailed',
variable_card=2,
values=[[0.9, 0.0], [0.1, 1.0]],
evidence=['FoundGuilty'],
evidence_card=[2])
#Associar os model aos nodos
election_model.add_cpds(cpd_b, cpd_i, cpd_m, cpd_g, cpd_j)
#Verificar as independencias
print(election_model.get_independencies())
samples = BayesianModelSampling(election_model).forward_sample(size=int(1e5))
samples.head()
#Mostrar estimativas
mle = MaximumLikelihoodEstimator(model=election_model, data=samples)
print("\nEstimating the CPD for a single node.\n")
print(mle.estimate_cpd(node='BrokeElectionLaw'))
print(mle.estimate_cpd(node='PoliticallyMotivatedProsecutor'))
print(mle.estimate_cpd(node='Indicted'))
print(mle.estimate_cpd(node='FoundGuilty'))
print(mle.estimate_cpd(node='Jailed'))
#Step 1: Generate some data
# Use the alarm model to generate data from it.
from pgmpy.utils import get_example_model
from pgmpy.sampling import BayesianModelSampling
alarm_model = get_example_model('alarm')
samples = BayesianModelSampling(alarm_model).forward_sample(size=int(1e5))
print(samples.head())
#Step 2: Define a model structure
# Defining the Bayesian Model structure
from pgmpy.models import BayesianModel
model_struct = BayesianModel(ebunch=alarm_model.edges())
print(model_struct.nodes())
#Step 3: Learning the model parameters
# Fitting the model using Maximum Likelihood Estimator
from pgmpy.estimators import MaximumLikelihoodEstimator
mle = MaximumLikelihoodEstimator(model=model_struct, data=samples)
# Estimating the CPD for a single node.
print(mle.estimate_cpd(node='FIO2'))
print(mle.estimate_cpd(node='CVP'))
# Estimating CPDs for all the nodes in the model
