title: Model selection and sensitivity analysis for sequence pattern models.
abstract: In this article we propose a maximal a posteriori (MAP) criterion for model selection in the motif discovery problem and investigate conditions under which the MAP asymptotically gives a correct prediction of model size. We also investigate robustness of the MAP to prior specification and provide guidelines for choosing prior hyper-parameters for motif models based on sensitivity considerations.
authors.affiliation: University of North Carolina at Chapel Hill.
authors.forename: Mayetri
authors.initials: M
authors.lastname: Gupta