License: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license (CC BY-NC-ND 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ICLP.2011.220
URN: urn:nbn:de:0030-drops-31649
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2011/3164/
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Christiansen, Henning ; Theil Have, Christian ; Torp Lassen, Ole ; Petit, Matthieu

Bayesian Annotation Networks for Complex Sequence Analysis

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Abstract

Probabilistic models that associate annotations to sequential data are widely used in computational biology and a range of other applications. Models integrating with logic programs provide, furthermore, for sophistication and generality, at the cost of potentially very high computational complexity. A methodology is proposed for modularization of such models into sub-models, each representing a particular interpretation of the input data to be analysed. Their composition forms, in a natural way, a Bayesian network, and we show how standard methods for prediction and training can be adapted for such composite models in an iterative way, obtaining reasonable complexity results. Our methodology can be implemented using the probabilistic-logic PRISM system, developed by Sato et al, in a way that allows for practical applications.

BibTeX - Entry

@InProceedings{christiansen_et_al:LIPIcs:2011:3164,
  author =	{Henning Christiansen and Christian Theil Have and Ole Torp Lassen and Matthieu Petit},
  title =	{{Bayesian Annotation Networks for Complex Sequence Analysis}},
  booktitle =	{Technical Communications of the 27th International Conference on Logic Programming (ICLP'11) },
  pages =	{220--230},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-31-6},
  ISSN =	{1868-8969},
  year =	{2011},
  volume =	{11},
  editor =	{John P. Gallagher and Michael Gelfond},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2011/3164},
  URN =		{urn:nbn:de:0030-drops-31649},
  doi =		{10.4230/LIPIcs.ICLP.2011.220},
  annote =	{Keywords: Probabilistic Logic Bayesian Sequence Analysis}
}

Keywords: Probabilistic Logic Bayesian Sequence Analysis
Collection: Technical Communications of the 27th International Conference on Logic Programming (ICLP'11)
Issue Date: 2011
Date of publication: 27.06.2011

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