>>> from pgmpy.factors.discrete import TabularCPD
>>> from pgmpy.utils import get_example_model

>>> alarm = get_example_model("alarm")
>>> cvp_evidence = TabularCPD(variable="CVP",
                          variable_card=3,
                          values=[[0.2], [0.3], [0.5]],
                          state_names={"CVP": ["LOW", "NORMAL", "HIGH"]})
>>> samples = alarm.simulate(n_samples=int(1e4), virtual_evidence=[cvp_evidence])

>>> samples.groupby(['CVP']).size() / samples.shape[0]
CVP
HIGH      0.2444
LOW       0.0736
NORMAL    0.6820
dtype: float64