Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging

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 struc...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2008-12, Vol.20 (12), p.1587-1600
Main Authors: Rui Chang, Stetter, M., Brauer, W.
Format: Article
Language:English
Subjects:
"fuzzy"
Algorithmics. Computability. Computer arithmetics
and probabilistic reasoning
Applications and Expert Knowledge-Intensive Systems
Applied sciences
Artificial intelligence
Bayesian analysis
Bayesian methods
Biological and medical sciences
Biological system modeling
Biology and genetics
Bones
Breast cancer
Computer science
control theory
systems
Computer simulation
Computer systems and distributed systems. User interface
Data processing. List processing. Character string processing
Exact sciences and technology
Inference
Joints
Knowledge engineering methodologies
Knowledge modeling
Lungs
Mathematical models
Medical sciences
Memory organisation. Data processing
Metastasis
Monte Carlo
Monte Carlo methods
Networks
Predictive models
Probabilistic algorithms
Probability
Probability and Statistics
Probability theory
Semantics
Software
Theoretical computing
Tumors
Uncertainty
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Summary: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.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2008.89