Bayesian Annotation Networks for Complex Sequence Analysis

Authors Henning Christiansen, Christian Theil Have, Ole Torp Lassen, Matthieu Petit

Thumbnail PDF


  • Filesize: 456 kB
  • 11 pages

Document Identifiers

Author Details

Henning Christiansen
Christian Theil Have
Ole Torp Lassen
Matthieu Petit

Cite AsGet BibTex

Henning Christiansen, Christian Theil Have, Ole Torp Lassen, and Matthieu Petit. Bayesian Annotation Networks for Complex Sequence Analysis. In Technical Communications of the 27th International Conference on Logic Programming (ICLP'11). Leibniz International Proceedings in Informatics (LIPIcs), Volume 11, pp. 220-230, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)


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.
  • Probabilistic Logic Bayesian Sequence Analysis


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads
Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail