LoopyBeliefPropagation Example 1: Equally Weighted Symptoms/Edges - Teradata Vantage

In this example, the probability of hepatitis depends on all symptoms equally; therefore, the edges table does not include edge weights.

Input

In the observation table, 't' means that the symptom is present and 'f' means that it is absent.

SQL Call

SELECT * FROM LoopyBeliefPropagation (
  ON lbp_edges AS edges PARTITION BY source
  ON lbp_vertices AS vertices PARTITION BY vertex
  ON lbp_observation AS observation PARTITION BY vertex
  USING
  TargetKey ('target')
  ObservationColumn ('obs')
  Accumulate ('vertex')
  MaxIterNum (20)
  StopThreshold (1E-10)
) AS dt ORDER BY vertex;

Output

In the output table, 1 means that the symptom is present and 0 means that it is absent. Five of the eight symptoms are present, so the conditional probability of hepatitis is 5/8 (0.625).