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This paper addresses learning stochastic rules especially on aninter-attribute relation based on a Minimum Description Length (MDL) principlewith a finite number of examples, assuming an application to the design ofintelligent relational database systems. The stochastic rule in this paperconsists of a model giving the structure like the dependencies of a BayesianBelief Network (BBN) and some stochastic parameters each indicating aconditional probability of an attribute value given the state determined by theother attributes' values in the same record. Especially, we propose theextended version of the algorithm of Chow and Liu in that our learningalgorithm selects the model in the range where the dependencies among theattributes are represented by some general plural number of trees.

A Construction of Bayesian Networks from Databases Based on an MDL Principle