Preprocessing for Optimization of Probabilistic-Logic Models for Sequence Analysis

Authors:
Henning Christiansen;Ole Torp Lassen
Affiliations:
Research group PLIS: Programming, Logic and Intelligent Systems Department of Communication, Business and Information Technologies, Roskilde University, Roskilde, Denmark DK-4000;Research group PLIS: Programming, Logic and Intelligent Systems Department of Communication, Business and Information Technologies, Roskilde University, Roskilde, Denmark DK-4000
Venue:
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Year:
2009

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Abstract

A class of probabilistic-logic models is considered, which increases the expressibility from HMM's and SCFG's regular and context-free languages to, in principle, Turing complete languages. In general, such models are computationally far too complex for direct use, so optimization by pruning and approximation are needed. The first steps are taken towards a methodology for optimizing such models by approximations using auxiliary models for preprocessing or splitting them into submodels. Evaluation of such approximating models is challenging as authoritative test data may be sparse. On the other hand, the original complex models may be used for generating artificial evaluation data by efficient sampling, which can be used in the evaluation, although it does not constitute a foolproof test procedure. These models and evaluation processes are illustrated in the PRISM system developed by other authors, and we discuss their applicability and limitations.