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To a large extent, probabilistic graphical models unify a great number of models from machine learning, statistical text processing, vision, bioinformatics, and many other fields concerned with the analysis and understanding of noisy, incomplete, or inconsistent data. Graphical models alleviate the complexity inherent to large dimensional statistical models (the so-called curse of dimensionality) by dividing the problem into a series of logically (and statistically) independent components. By factoring the problem into sub-problems with known and simple interdependencies, and by adopting a common language to describe each sub-problem, one can considerably simplify the task of creating complex Bayesian models. Modularity can be taken advantage of further by leveraging this modeling hierarchy over several levels (e.g. a sub-model can also be decomposed into a family of sub-sub-models).
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