Algorithms for variable length Markov chain modeling.
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.


BACKGROUND
The source code is freely available at http://www.soe.ucsc.edu/~jill/src/
Bejerano
Gill
G
Center for Biomolecular Science and Engineering, School of Engineering, University of California, Santa Cruz, CA 95064, USA. jill@soe.ucsc.edu
D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D016415:Sequence Alignment / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods*; D017385:Sequence Homology; D012984:Software