Abstract
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.
Original language | English (US) |
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Article number | 4515867 |
Pages (from-to) | 1587-1600 |
Number of pages | 14 |
Journal | IEEE Transactions on Knowledge and Data Engineering |
Volume | 20 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2008 |
Externally published | Yes |
Keywords
- Bayesian model averaging
- Bayesian network inference
- Bayesian networks
- Bone metastasis
- Breast cancer
- Monte Carlo simulation
- Qualitative knowledge modeling
- Qualitative probabilistic network
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics