Inference with Causal Independence in the CPSC Network
This work demonstrates the algorithm's effectiveness on a specific network, but it is incremental as it tests an existing method on new data without novel contributions.
The paper applied the causal independence inference algorithm to the CPSC network, achieving high accuracy on zero-observation queries (420 out of 422) but decreasing performance with more observations, such as 94 out of 100 for five-observation queries.
This paper reports experiments with the causal independence inference algorithm proposed by Zhang and Poole (1994b) on the CPSC network created by Pradhan et al. (1994). It is found that the algorithm is able to answer 420 of the 422 possible zero-observation queries, 94 of 100 randomly generated five-observation queries, 87 of 100 randomly generated ten-observation queries, and 69 of 100 randomly generated twenty-observation queries.