Given a training set of observed states, x_1^{n}, the construction algorithm of the VOM models[REF][REF][REF] learns a model P that provides a probability assignment for each state in the sequence given its past (previously observed symbols) or future states.
VOM models attempt to estimate conditional distributions of the form P(x_i|s) where the context length |s|≤D varies depending on the available statistics.