A Construction of Bayesian Networks from Databases Based on an MDL
  Principle
  This paper addresses learning stochastic rules especially on an
inter-attribute relation based on a Minimum Description Length (MDL) principle
with a finite number of examples, assuming an application to the design of
intelligent relational database systems. The stochastic rule in this paper
consists of a model giving the structure like the dependencies of a Bayesian
Belief Network (BBN) and some stochastic parameters each indicating a
conditional probability of an attribute value given the state determined by the
other attributes' values in the same record. Especially, we propose the
extended version of the algorithm of Chow and Liu in that our learning
algorithm selects the model in the range where the dependencies among the
attributes are represented by some general plural number of trees.
