AIMar 27, 2013
MCE Reasoning in Recursive Causal NetworksWilson X. Wen
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.
AIMar 27, 2013
Directed Cycles in Belief NetworksWilson X. Wen
The most difficult task in probabilistic reasoning may be handling directed cycles in belief networks. To the best knowledge of this author, there is no serious discussion of this problem at all in the literature of probabilistic reasoning so far.
AIMar 27, 2013
Optimal Decomposition of Belief NetworksWilson X. Wen
In this paper, optimum decomposition of belief networks is discussed. Some methods of decomposition are examined and a new method - the method of Minimum Total Number of States (MTNS) - is proposed. The problem of optimum belief network decomposition under our framework, as under all the other frameworks, is shown to be NP-hard. According to the computational complexity analysis, an algorithm of belief network decomposition is proposed in (Wee, 1990a) based on simulated annealing.
AIMar 20, 2013
From Relational Databases to Belief NetworksWilson X. Wen
The relationship between belief networks and relational databases is examined. Based on this analysis, a method to construct belief networks automatically from statistical relational data is proposed. A comparison between our method and other methods shows that our method has several advantages when generalization or prediction is deeded.