Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Physical network models and multi-source data integration
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Nonparametric belief propagation for self-calibration in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
COPASI---a COmplex PAthway SImulator
Bioinformatics
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The factor graph network model for biological systems
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
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Parameter estimation of large bio-pathway models is an important and difficult problem. To reduce the prohibitive computational cost, one approach is to decompose a large model into components and estimate their parameters separately. However, the decomposed components often share common parts that may have conflicting parameter estimates, as they are computed independently within each component. In this paper, we propose to use a probabilistic inference technique called belief propagation to reconcile these independent estimates in a principled manner and compute new estimates that are globally consistent and fit well with data. An important advantage of our approach in practice is that it naturally handles incomplete or noisy data. Preliminary results based on synthetic data show promising performance in terms of both accuracy and efficiency.