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28 March 2024
 
  » arxiv » q-bio.GN/0401033

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Parametric Inference for Biological Sequence Analysis
Lior Pachter ; Bernd Sturmfels ;
Date 26 Dec 2003
Subject Genomics; Statistics; Learning | q-bio.GN cs.LG math.ST
AbstractOne of the major successes in computational biology has been the unification, using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied towards these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems associated with different statistical models. This paper introduces the emph{polytope propagation algorithm} for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.
Source arXiv, q-bio.GN/0401033
Other source [GID 132834] pnas
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