BayesiaLab 3.2 : new features

• A priori knwoledge expressed with an initial Bayesian network is taken into account by all the learning algorithms
• New analysis report that returns a numerical measure of the importance of the probabilistic relations (arcs). This report then complete the visual analysis based on the thikness of the arcs
• New missing values imputation tool that use all the available information to complete missing data in a rigourous fashion
• Confidence measure of the automatically estimated probabilities by displaying the number of examples used for the estimation of each probability
• New symmetric algorithm for a very efficient layouting of the networks that are moderatly connected
• New search tool that allows searching nodes and arcs that can be described with meta characters
• Arcs can have associated color tags and comments
• Improvement of the importation wizard for data coming from Data Bases
• New tool for the generation of data corresponding to a Bayesian network that includes the possibility to specify a percentage of missing values