LoopyBeliefPropagation Example 2: Unequally Weighted Symptoms/Edges - Teradata Vantage

In this example, the probability of hepatitis depends more on some symptoms than others; therefore, the edges table includes edge weights (and the SQL call includes the EdgeWeight argument).

Input

• From LoopyBeliefPropagation Example 1: Equally Weighted Symptoms/Edges:
  • vertices: lbp_vertices
  • observation: lbp_observation
• edges: lbp_weighted_edges

SQL Call

SELECT * FROM LoopyBeliefPropagation (
  ON lbp_weighted_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')
  EdgeWeight ('edgewt')
  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. The conditional probability of hepatitis is the sum of the weights of the symptoms that are present (0.25 + 0.1 + 0.05 + 0.15 + 0.2 = 0.75).