Though classification algorithms succeed in ignoring background data for the sake of efficiency, most tasks require a complete understanding of input data, and are thus more computationally expensive. Modeling a distribution with a lookup table in the absence of other processing creates issues of memory and efficiency. These issues can be minimized through identifying and only modeling direct - and not indirect - interactions between variables, i.e. Structured Modeling.