This paper presents three new algorithms for the automatic construction of models from Object Oriented Probabilistic RelationalModels. The first two algorithms are based on the kn...
This paper presents a number of new algorithms for discovering the Markov Blanket of a target variable T from training data. The Markov Blanket can be used for variable selection ...
Ioannis Tsamardinos, Constantin F. Aliferis, Alexa...
We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood learning of Bayesian networks with belief propagation algorithms for approximate i...
In recent years, there have been several proposals that extend the expressive power of Bayesian networks with that of relational models. These languages open the possibility for t...
We prove that perfect distributions exist when using a finite number of bits to represent the parameters of a Bayesian network. In addition, we provide an upper bound on the prob...