title: Evaluation and comparison of computational models.
abstract: Computational models are powerful tools that can enhance the understanding of scientific phenomena. The enterprise of modeling is most productive when the reasons underlying the adequacy of a model, and possibly its superiority to other models, are understood. This chapter begins with an overview of the main criteria that must be considered in model evaluation and selection, in particular explaining why generalizability is the preferred criterion for model selection. This is followed by a review of measures of generalizability. The final section demonstrates the use of five versatile and easy-to-use selection methods for choosing between two mathematical models of protein folding.
authors.affiliation: Department of Psychology, Ohio State University, Columbus, Ohio, USA.
authors.forename: Jay I
authors.initials: JI
authors.lastname: Myung
authors.forename: Yun
authors.initials: Y
authors.lastname: Tang
authors.forename: Mark A
authors.initials: MA
authors.lastname: Pitt
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D017510:Protein Folding