Inhomogeneous Parsimonious Markov Models
Ralf Eggeling, Andre Gohr, Pierre-Yves Bourguignon, Edgar Wingender, and Ivo Grosse
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Submission date: 14. Aug. 2013
published in: Machine learning and knowledge discovery in databases : European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part 1 / H. Blockeel ... (eds.)
Berlin [u. a.] : Springer, 2013. - P. 321 - 336
(Lecture notes in artificial intelligence ; 8188)
DOI number (of the published article): 10.1007/978-3-642-40988-2_21
Keywords and phrases: Parsimonious Markov models, Sequence analysis, Statistical dependencies
We introduce inhomogeneous parsimonious Markov models for modeling statistical patterns in discrete sequences. These models are based on parsimonious context trees, which are a generalization of con- text trees, and thus generalize variable order Markov models. We follow a Bayesian approach, consisting of structure and parameter learning. Structure learning is a challenging problem due to an overexponential number of possible tree structures, so we describe an exact and eﬃcient dynamic programming algorithm for ﬁnding the optimal tree structures. We apply model and learning algorithm to the problem of model- ing binding sites of the human transcription factor C/EBP, and ﬁnd an increased prediction performance compared to ﬁxed order and variable order Markov models. We investigate the reason for this improvement and ﬁnd several instances of context-speciﬁc dependences that can be captured by parsimonious context trees but not by traditional context trees.