  Probabilistic graphical models compactly represent joint distributions by
decomposing them into factors over subsets of random variables. In Bayesian
networks, the factors are conditional probability distributions. For many
problems, common information exists among those factors. Adding similarity
restrictions can be viewed as imposing prior knowledge for model
regularization. With proper restrictions, learned models usually generalize
better. In this work, we study methods that exploit such high-level
similarities to regularize the learning process and apply them to the task of
modeling the wave propagation in inhomogeneous media. We propose a novel
distribution-based penalization approach that encourages similar conditional
probability distribution rather than force the parameters to be similar
explicitly. We show in experiment that our proposed algorithm solves the
modeling wave propagation problem, which other baseline methods are not able to
solve.
