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MiS Preprint

Inhomogeneous Parsimonious Markov Models

Ralf Eggeling, Andre Gohr, Pierre-Yves Bourguignon, Edgar Wingender and Ivo Grosse


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 context 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 modeling 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.

Aug 14, 2013
Sep 4, 2013
Parsimonious Markov models, Sequence analysis, Statistical dependencies

Related publications

2013 Repository Open Access
Ralf Eggeling, André Gohr, Pierre-Yves Bourguignon, Edgar Wingender and Ivo Grosse

Inhomogeneous parsimonious Markov models

In: Machine learning and knowledge discovery in databases : European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part 1 / Hendrik Blockeel... (eds.)
Berlin [u. a.] : Springer, 2013. - pp. 321-336
(Lecture notes in artificial intelligence ; 8188)