Sampling of data is based on the joint distribution of the network. In order to do that, it requires a DAG connected with CPDs. It is also possible to create a DAG manually and learn it’s model parameters.

Forward Sampling

# Import library
import bnlearn as bn

# Load example DAG with CPD
model = bn.import_DAG('sprinkler', CPD=True)

# Take 1000 samples from the CPD distribution
df = bn.sampling(model, n=1000, methodtype='bayes')

df.head()

Cloudy	Sprinkler	Rain	Wet_Grass
0	1	0	1
1	1	1	1
1	0	1	1
…	…	…	…
0	0	0	0
1	0	0	0
1	0	1	1

Gibbs Sampling

# Import library
import bnlearn as bn

# Load example DAG with CPD
model = bn.import_DAG('sprinkler', CPD=True)

# Take 1000 samples from the CPD distribution
df = bn.sampling(model, n=1000, methodtype='gibbs')

df.head()

Another example with Gibbs sampling but now by creating some user-defined edges

# Load example dataset
df = bn.import_example('sprinkler')

# Create some edges
edges = [('Cloudy', 'Sprinkler'),
        ('Cloudy', 'Rain'),
        ('Sprinkler', 'Wet_Grass'),
        ('Rain', 'Wet_Grass')]

# Make the actual Bayesian DAG
DAG = bn.make_DAG(edges, methodtype='bayes', verbose=3)

# Fit model
model = bn.parameter_learning.fit(DAG, df, verbose=3, methodtype='bayes')

# Sampling using gibbs
df = bn.sampling(model, n=100, methodtype='gibbs', verbose=3)

Cloudy	Sprinkler	Rain	Wet_Grass
0	1	0	1
1	0	0	1
1	0	1	1
…	…	…	…
0	0	0	0
1	0	0	0
1	0	1	1

---

---