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 the relationship between at least two variable nodes; training structure and parameterization of each regression tree using training data having a plurality of datasets, each dataset having elements of at least one labeled property, the training executing an objective function; and storing the trained factor graph in processor-readable memory.