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In the example below the specified virtual evidence for CVP doesn't match the marginal distribution when computed from the samples.
>>> 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
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In the example below the specified virtual evidence for CVP doesn't match the marginal distribution when computed from the samples.
The text was updated successfully, but these errors were encountered: