title: Parametric inference for biological sequence analysis.
abstract: One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to 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 that are associated with different statistical models. This article introduces the 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.
authors.affiliation: Department of Mathematics, University of California, Berkeley, CA 94720, USA. lpachter@math.berkeley.edu
authors.forename: Lior
authors.initials: L
authors.lastname: Pachter
authors.forename: Bernd
authors.initials: B
authors.lastname: Sturmfels
mesh_terms: D000465:Algorithms; D008390:Markov Chains; D015233:Models, Statistical; D016415:Sequence Alignment / Q000706:statistics & numerical data; D017421:Sequence Analysis / Q000706:statistics & numerical data*; D017422:Sequence Analysis, DNA / Q000706:statistics & numerical data