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Numerical Solutions for Patterns Statistics on Markov Chains

Author

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  • Nuel Gregory

    (Laboratoire Statistique et Genome, CNRS (8071), INRA (1152), UEVE, Evry, France)

Abstract

We propose here a review of the methods available to compute pattern statistics on text generated by a Markov source. Theoretical, but also numerical aspects are detailed for a wide range of techniques (exact, Gaussian, large deviations, binomial and compound Poisson). The SPatt package (Statistics for Pattern, free software available at http://stat.genopole.cnrs.fr/spatt) implementing all these methods is then used to compare all these approaches in terms of computational time and reliability in the most complete pattern statistics benchmark available at the present time.

Suggested Citation

  • Nuel Gregory, 2006. "Numerical Solutions for Patterns Statistics on Markov Chains," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-45, October.
    • Handle: RePEc:bpj:sagmbi:v:5:y:2006:i:1:n:26
    DOI: 10.2202/1544-6115.1219
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    Cited by:

    1. Ana Helena Tavares & Jakob Raymaekers & Peter J. Rousseeuw & Paula Brito & Vera Afreixo, 2020. "Clustering genomic words in human DNA using peaks and trends of distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 57-76, March.
    2. Singer Meromit & Engström Alexander & Schönhuth Alexander & Pachter Lior, 2011. "Determining Coding CpG Islands by Identifying Regions Significant for Pattern Statistics on Markov Chains," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-27, September.

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