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Model selection and sensitivity analysis for sequence pattern models
Mayetri Gupta
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.
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Primary Subjects: 62F15, 62P10
Secondary Subjects: 62F12
Keywords: Bayes factor; MAP; model selection; motif discovery
Full-text: Open access
Links and Identifiers
Permanent link to this document: http://projecteuclid.org/euclid.imsc/1207058288
Digital Object Identifier: doi:10.1214/193940307000000301
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