Sparse Markov Chains for Sequence Data. Scandinavian Journal of Statistics

Jääskinen, Väinö (author)

Xiong, Jie (author)

Corander, Jukka (author)

(creator_code:org_t)

2013-10-31
2014
English.
In: Scandinavian Journal of Statistics. - New York : John Wiley & Sons, Inc.. - 0303-6898 .- 1467-9469. ; 41:3, s. 639-655

Related links:: https://urn.kb.se/re...; show more...; https://doi.org/10.1...; show less...

Abstract Subject headings

Finite memory sources and variable-length Markov chains have recently gained popularity in data compression and mining, in particular, for applications in bioinformatics and language modelling. Here, we consider denser data compression and prediction with a family of sparse Bayesian predictive models for Markov chains in finite state spaces. Our approach lumps transition probabilities into classes composed of invariant probabilities, such that the resulting models need not have a hierarchical structure as in context tree-based approaches. This can lead to a substantially higher rate of data compression, and such non-hierarchical sparse models can be motivated for instance by data dependence structures existing in the bioinformatics context. We describe a Bayesian inference algorithm for learning sparse Markov models through clustering of transition probabilities. Experiments with DNA sequence and protein data show that our approach is competitive in both prediction and classification when compared with several alternative methods on the basis of variable memory length.

By the author/editor: Jääskinen, Väinö; Xiong, Jie; Corander, Jukka; Koski, Timo

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Mathematics; and Probability Theo ...

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