B-COURSE: A WEB-BASED TOOL FOR BAYESIAN AND CAUSAL DATA ANALYSIS
- 1 September 2002
- journal article
- research article
- Published by World Scientific Pub Co Pte Ltd in International Journal on Artificial Intelligence Tools
- Vol. 11 (03) , 369-387
- https://doi.org/10.1142/s0218213002000940
Abstract
B-Course is a free web-based online data analysis tool, which allows the users to analyze their data for multivariate probabilistic dependencies. These dependencies are represented as Bayesian network models. In addition to this, B-Course also offers facilities for inferring certain type of causal dependencies from the data. The software uses a novel "tutorial stylerdquo; user-friendly interface which intertwines the steps in the data analysis with support material that gives an informal introduction to the Bayesian approach adopted. Although the analysis methods, modeling assumptions and restrictions are totally transparent to the user, this transparency is not achieved at the expense of analysis power: with the restrictions stated in the support material, B-Course is a powerful analysis tool exploiting several theoretically elaborate results developed recently in the fields of Bayesian and causal modeling. B-Course can be used with most web-browsers (even Lynx), and the facilities include features such as automatic missing data handling and discretization, a flexible graphical interface for probabilistic inference on the constructed Bayesian network models (for Java enabled browsers), automatic prettyHyphen;printed layout for the networks, exportation of the models, and analysis of the importance of the derived dependencies. In this paper we discuss both the theoretical design principles underlying the B-Course tool, and the pragmatic methods adopted in the implementation of the software.Keywords
This publication has 5 references indexed in Scilit:
- On predictive distributions and Bayesian networksStatistics and Computing, 2000
- Fisher information and stochastic complexityIEEE Transactions on Information Theory, 1996
- On compatible priors for Bayesian networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- Jeffreys' prior is asymptotically least favorable under entropy riskJournal of Statistical Planning and Inference, 1994
- An invariant form for the prior probability in estimation problemsProceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 1946