Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
On inclusion-driven learning of bayesian networks
The Journal of Machine Learning Research
Exact Bayesian Structure Discovery in Bayesian Networks
The Journal of Machine Learning Research
A transformational characterization of equivalent Bayesian network structures
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
EURASIP Journal on Bioinformatics and Systems Biology
Ancestor relations in the presence of unobserved variables
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Effective connectivity analysis of fMRI and MEG data collected under identical paradigms
Computers in Biology and Medicine
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
New skeleton-based approaches for Bayesian structure learning of Bayesian networks
Applied Soft Computing
Parallel globally optimal structure learning of Bayesian networks
Journal of Parallel and Distributed Computing
Annealed importance sampling for structure learning in Bayesian networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Applications of Bayesian networks in systems biology are computationally demanding due to the large number of model parameters. Conventional MCMC schemes based on proposal moves in structure space tend to be too slow in mixing and convergence, and have recently been superseded by proposal moves in the space of node orders. A disadvantage of the latter approach is the intrinsic inability to specify the prior probability on network structures explicitly. The relative paucity of different experimental conditions in contemporary systems biology implies a strong influence of the prior probability on the posterior probability and, hence, the outcome of inference. Consequently, the paradigm of performing MCMC proposal moves in order rather than structure space is not entirely satisfactory. In the present article, we propose a new and more extensive edge reversal move in the original structure space, and we show that this significantly improves the convergence of the classical structure MCMC scheme.