python pomegranate bayesian network initialization

Question

I have using this module to train a bayesian network.

I have this csv:

c1, c2, c3, c4   # the columns names
1, 0, 0, 1
1, 0, 1, 1
1, 1, 0, 0
0, 0, 1, 0
0, 1, 0, 0
.
.
.

I I have the edges of the network. I know that c1 -> c3, c2->c3, c2->c4.

How can I build a bayesian network using pomegranate? All of the documentation that i find for example (take from the official website):

guest = DiscreteDistribution({'A': 1./3, 'B': 1./3, 'C': 1./3})
prize = DiscreteDistribution({'A': 1./3, 'B': 1./3, 'C': 1./3})
monty = ConditionalProbabilityTable(
        [['A', 'A', 'A', 0.0],
         ['A', 'A', 'B', 0.5],
         ['A', 'A', 'C', 0.5],
         ['A', 'B', 'A', 0.0],
         ['A', 'B', 'B', 0.0],
         ['A', 'B', 'C', 1.0],
         ['A', 'C', 'A', 0.0],
         ['A', 'C', 'B', 1.0],
         ['A', 'C', 'C', 0.0],
         ['B', 'A', 'A', 0.0],
         ['B', 'A', 'B', 0.0],
         ['B', 'A', 'C', 1.0],
         ['B', 'B', 'A', 0.5],
         ['B', 'B', 'B', 0.0],
         ['B', 'B', 'C', 0.5],
         ['B', 'C', 'A', 1.0],
         ['B', 'C', 'B', 0.0],
         ['B', 'C', 'C', 0.0],
         ['C', 'A', 'A', 0.0],
         ['C', 'A', 'B', 1.0],
         ['C', 'A', 'C', 0.0],
         ['C', 'B', 'A', 1.0],
         ['C', 'B', 'B', 0.0],
         ['C', 'B', 'C', 0.0],
         ['C', 'C', 'A', 0.5],
         ['C', 'C', 'B', 0.5],
         ['C', 'C', 'C', 0.0]], [guest, prize])

s1 = Node(guest, name="guest")
s2 = Node(prize, name="prize")
s3 = Node(monty, name="monty")

model = BayesianNetwork("Monty Hall Problem")
model.add_states(s1, s2, s3)
model.add_edge(s1, s3)
model.add_edge(s2, s3)
model.bake()

There is no way to build a state if the probabilities are unknown how can /i do that?