Issue No. 12 - December (2008 vol. 20)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.89
Rui Chang , Technical University Munich, Munich
Martin Stetter , SIEMENS AG., Munich
Wilfried Brauer , Institue of Informatics, Munich
In this paper, we consider the problem of performing quantitative Bayesian inference and model averaging based on a set of qualitative statements about relationships. Statements are transformed into parameter constraints which are imposed onto a set of Bayesian networks. Recurrent relationship structures are resolved by unfolding in time to Dynamic Bayesian networks. The approach enables probabilistic inference by model averaging, i.e. it allows to predict probabilistic quantities from a set of qualitative constraints without probability assignment on the model parameters. Model averaging is performed by Monte Carlo integration techniques. The method is applied to a problem in a molecular medical context: We show how the rate of breast cancer metastasis formation can be predicted based solely on a set of qualitative biological statements about the involvement of proteins in metastatic processes.
Probability and Statistics, Probabilistic algorithms, Uncertainty, "fuzzy", and probabilistic reasoning, Monte Carlo, Applications and Expert Knowledge-Intensive Systems, Knowledge modeling, Knowledge engineering methodologies, Biology and genetics
M. Stetter, W. Brauer and R. Chang, "Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging," in IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. , pp. 1587-1600, 2008.