Reconstruction of plant–pollinator networks from observational data

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

Learning Deterministic Causal Networks from Observational Data

Previous work suggests that humans find it difficult to learn the structure of causal systems given observational data alone. We show that structure learning is successful when the causal systems in question are consistent with people’s expectations that causal relationships are deterministic and that each pattern of observations has a single underlying cause. Our data are well explained by a B...

image reconstruction from incomplete data

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Multivariate construction of effective computational networks from observational data

We introduce a new method for inferring effective network structure given a multivariate timeseries of activation levels of the nodes in the network. For each destination node in the network, the method identifies the set of source nodes which can be used to provide the most statistically significant information regarding outcomes of the destination, and are thus inferred as those source inform...