Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Random generation of dags for graph drawing
Random generation of dags for graph drawing
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
The Journal of Machine Learning Research
Permutation testing improves Bayesian network learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Towards integrative causal analysis of heterogeneous data sets and studies
The Journal of Machine Learning Research
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There are well known algorithms for learning the structure of directed and undirected graphical models from data, but nearly all assume that the data consists of a single i.i.d. sample. In contexts such as fMRI analysis, data may consist of an ensemble of independent samples from a common data generating mechanism which may not have identical distributions. Pooling such data can result in a number of well known statistical problems so each sample must be analyzed individually, which offers no increase in power due to the presence of multiple samples. We show how existing constraint based methods can be modified to learn structure from the aggregate of such data in a statistically sound manner. The prescribed method is simple to implement and based on existing statistical methods employed in metaanalysis and other areas, but works surprisingly well in this context where there are increased concerns due to issues such as retesting. We report results for directed models, but the method given is just as applicable to undirected models.