  Model selection and learning the structure of graphical models from the data
sample constitutes an important field of probabilistic graphical model
research, as in most of the situations the structure is unknown and has to be
learnt from the given dataset. In this paper, we present a new forward model
selection algorithm for graphical log-linear models. We use mutual conditional
independence check to reduce the search space which also takes care of the
evaluation of the joint effects and chances of missing important interactions
are eliminated. We illustrate our algorithm with a real dataset example.
