In recent years, a number of algorithms have been developed for learning the structure of Bayesian networks from data. In this paper we apply some of these algorithms to a realist...
Xiaofeng Wu, Peter J. F. Lucas, Susan Kerr, Roelf ...
The paper presents a method for pruning frequent itemsets based on background knowledge represented by a Bayesian network. The interestingness of an itemset is defined as the abso...
A Bayesian network is an appropriate tool to deal with the uncertainty that is typical of real-life applications. Bayesian network arcs represent statistical dependence between dif...
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...
Because of practical limits in characterizing the safety profiles of therapeutic products prior to marketing, manufacturers and regulatory agencies perform post-marketing surveill...
David M. Fram, June S. Almenoff, William DuMouchel