MCE Reasoning in Recursive Causal Networks
This work addresses uncertainty reasoning for AI and probabilistic modeling, but appears incremental as it builds on existing principles like MCE and causal networks.
The paper tackles reasoning under uncertainty by proposing a probabilistic method based on Minimum Cross Entropy and Recursive Causal Models, using a Belief Networks Description Language (BNDL) for variable dependencies and developing interpreters in Prolog and C to compare performance with other methods.
A probabilistic method of reasoning under uncertainty is proposed based on the principle of Minimum Cross Entropy (MCE) and concept of Recursive Causal Model (RCM). The dependency and correlations among the variables are described in a special language BNDL (Belief Networks Description Language). Beliefs are propagated among the clauses of the BNDL programs representing the underlying probabilistic distributions. BNDL interpreters in both Prolog and C has been developed and the performance of the method is compared with those of the others.