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Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging
December 2008 (vol. 20 no. 12)
pp. 1587-1600
ASCII Text
x 
 
Rui Chang, Martin Stetter, Wilfried Brauer, "Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 12, pp. 1587-1600, December, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2008.89,
author = {Rui Chang and Martin Stetter and Wilfried Brauer},
title = {Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {20},
number = {12},
issn = {1041-4347},
year = {2008},
pages = {1587-1600},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.89},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging
IS - 12
SN - 1041-4347
SP1587
EP1600
EPD - 1587-1600
A1 - Rui Chang,
A1 - Martin Stetter,
A1 - Wilfried Brauer,
PY - 2008
KW - Probability and Statistics
KW - Probabilistic algorithms
KW - Uncertainty
KW - "fuzzy"
KW - and probabilistic reasoning
KW - Monte Carlo
KW - Applications and Expert Knowledge-Intensive Systems
KW - Knowledge modeling
KW - Knowledge engineering methodologies
KW - Biology and genetics
VL - 20
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
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.

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Index Terms:
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
Citation:
Rui Chang, Martin Stetter, Wilfried Brauer, "Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 12, pp. 1587-1600, Dec. 2008, doi:10.1109/TKDE.2008.89
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