Abstract. Dynamic Bayesian networks (DBNs) extend Bayesian networks from static domains to dynamic domains. The only known generic method for exact inference in DBNs is based on dy...
: This paper presents a web supported development tool specific for medical diagnosis, based on Bayesian networks. The proposed system provides a user-friendly interface, giving th...
Abstract. Due to the uncertain nature of many of the factors that influence on the performance of an emergency medical service, we propose using Bayesian networks to model this ki...
Silvia Acid, Luis M. de Campos, Susana Rodrí...
This paper describes a novel data mining approach that employs evolutionary programming to discover knowledge represented in Bayesian networks. There are two different approaches ...
We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network con...
Seiya Imoto, SunYong Kim, Takao Goto, Sachiyo Abur...
—Recently, Bayesian probabilistic models have been used for predicting software development effort. One of the reasons for the interest in the use of Bayesian probabilistic model...
Parag C. Pendharkar, Girish H. Subramanian, James ...
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of the...
In complex distributed applications, a problem is often decomposed into a set of subproblems that are distributed to multiple agents. We formulate this class of problems with a tw...
A bayesian network is an appropriate tool for working with uncertainty and probability, that are typical of real-life applications. In literature we find different approaches for b...
Evelina Lamma, Fabrizio Riguzzi, Andrea Stambazzi,...
The assessment of a probability distribution associated with a Bayesian network is a challenging task, even if its topology is sparse. Special probability distributions based on t...