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Using Dirichlet mixture priors to derive hidden Markov models for protein families
Proceedings. International Conference on Intelligent Systems for Molecular Biology
A Bayesian method for estimating the amino acid distributions in the states of a hidden Markov model (HMM) for a protein family or the columns of a multiple alignment of that family is introduced. This method uses Dirichlet mixture densities as priors over amino acid distributions. These mixture densities are determined from examination of previously constructed HMMs or multiple alignments. It is shown that this Bayesian method can improve the quality of HMMs produced from small training sets.pmid:7584370 fatcat:6gdt6fwlwfa5liv3b5l7y3tpmq