Introduction to algorithms
A well-characterized approximation problem
Information Processing Letters
Tree adjoining grammars for RNA structure prediction
Theoretical Computer Science - Special issue: Genome informatics
Metrics and Similarity Measures for Hidden Markov Models
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Two Methods for Improving Performance of a HMM and their Application for Gene Finding
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
A Hidden Markov Model for Predicting Transmembrane Helices in Protein Sequences
ISMB '98 Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology
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Stochastic models are commonly used in bioinformatics, e.g., hidden Markov models for modeling sequence families or stochastic context-free grammars for modeling RNA secondary structure formation. Comparing data is a common task in bioinformatics, and it is thus natural to consider how to compare stochastic models. In this paper we present the first study of the problem of comparing a hidden Markov model and a stochastic context-free grammar. We describe how to compute their co-emission--or collision--probability, i.e., the probability that they independently generate the same sequence. We also consider the related problem of finding a run through a hidden Markov model and derivation in a grammar that generate the same sequence and have maximal joint probability by a generalization of the CYK algorithm for parsing a sequence by a stochastic context-free grammar. We illustrate the methods by an experiment on RNA secondary structures.