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An Efficient Algorithm for Learning Bayesian Networks from Data

Dojer N.

Fundamenta Informaticae

2010

Vol. 103, nr 1-4

53-67

We propose an algorithm for learning an optimal Bayesian network from data. Our method is addressed to biological applications, where usually datasets are small, and sets of random variables are large. Moreover, we assume that there is no need to examine the acyclicity of the graph. We provide polynomial bounds (with respect to the number of random variables) for time complexity of our algorithm for two generally used scoring criteria: Minimal Description Length and Bayesian- Dirichlet equivalence. Then we show how to adapt these criteria to work with continuous data and prove polynomial bounds for adapted scores. Finally, we briefly describe applications of proposed algorithm in computational biology.

Applying term rewriting to partial algebra theory

Dojer N.

Fundamenta Informaticae

2004

Vol. 63, nr 4

375--384

In our paper we apply term rewriting to decidability of equations in classes of partial algebras, as well as to embeddability of partial algebras into classes of total ones. We follow the approach to partial algebras introduced by H.-J. Kreowski in [13], in which (classes of) partial algebras are described by means of (classes of) total ones.