Bioinformatics Advance Access originally published online on January 29, 2004
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Bioinformatics 20(5) © Oxford University Press 2004; all rights reserved.
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Algorithms for variable length Markov chain modeling
Center for Biomolecular Science and Engineering, School of Engineering, University of California, Santa Cruz, CA 95064, USA
Received on August 4, 2003
; revised on September 30, 2003
; accepted on October 15, 2003
Advance Access Publication January 29, 2004
Summary: We present a general purpose implementation of variable length Markov models. Contrary to fixed order Markov models, these models are not restricted to a predefined uniform depth. Rather, by examining the training data, a model is constructed that fits higher order Markov dependencies where such contexts exist, while using lower order Markov dependencies elsewhere. As both theoretical and experimental results show, these models are capable of capturing rich signals from a modest amount of training data, without the use of hidden states.
Availability: The source code is freely available at http://www.soe.ucsc.edu/~jill/src/
Contact: jill{at}soe.ucsc.edu
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