We present a new approach to structure learning in the field of Bayesian networks. We
tackle the problem of the search for the best Bayesian network structure, given a database of …

We consider the task of learning the maximum-likelihood polytree from data. Our first result
is a performance guarantee establishing that the optimal branching (or Chow-Liu tree) …

Previous algorithms for the construction of belief networks structures from data are mainly
based either on independence criteria or on scoring metrics. The aim of this paper is to …

Graphical structures such as Bayesian networks or Markov networks are very useful tools for
representing irrelevance or independency relationships, and they may be used to efficiently …

Causal concepts play a crucial role in many reasoning tasks. Organised as a model
revealing the causal structure of a domain, they can guide inference through relevant …

A new structure learning approach for Bayesian networks (BNs) based on dual genetic
algorithm (DGA) is proposed in this paper. An individual of the population is represented as …

Once causal networks have been chosen as the model of knowledge representation of our
interest, the aim of this work is to assess the performance of polytrees or Singly connected …

In this paper, a novel method for structure learning of a Bayesian network (BN) is developed.
A new genetic approach called the matrix genetic algorithm (MGA) is proposed. In this …

The essence of causality can be identified with a graphical structure representing relevance
relationships between variables. In this paper the problem of infering causal relations from …

In spite of many useful properties, the Dempster-Shafer Theory of evidence (DST)
experienced sharp criticism from many sides. The basic line of criticism is connected with the …