Abstract: The problem of hypothesis testing against independence for a Gauss–Markov random field (GMRF) is analyzed. Assuming an acyclic dependency graph, an expression for the log-likelihood ratio of ...

GRACE exploits biological a priori and heterogeneous data integration to generate high-confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion.

Second, a group of straight lines that represent roof boundary sides and roof ridgelines of a selected building is obtained through the optimization of a Markov random field-based energy function ...