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Probabilistic models that associate annotations to sequential data are widely used in computational biology and a range of other applications. Models integrating with logic programs provide, furthermore, for sophistication and generality, at the cost of potentially very high computational complexity. A methodology is proposed for modularization of such models into sub-models, each representing a particular interpretation of the input data to be analyzed. The authors' composition forms, in a natural way, a Bayesian network, and they show how standard methods for prediction and training can be adapted for such composite models in an iterative way, obtaining reasonable complexity results.
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