1. A method, comprising: defining in processor-readable memory a factor graph including a factor graph model replicated for each variable node of a set of variable nodes, each variable node being informed by one or more factors in the factor graph model, each factor being implemented as a regression tree, at least one factor defining a relationship between at least two variable nodes in the set of variable nodes; training structure and parameterization of regression trees of the factor graph using an objective function and training data having a plurality of datasets, each dataset having elements of at least one labeled property, wherein the training determines parameters of energy functions for leaf nodes of the regression trees; and storing the factor graph as trained in the processor-readable memory.