Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions
IEEE Transactions on Knowledge and Data Engineering
Fast and Adaptive Variable Order Markov Chain Construction
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Computational Biology and Chemistry
A robust method for transcript quantification with RNA-seq data
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
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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/