title: Choice of summary statistic weights in approximate Bayesian computation.
abstract: In this paper, we develop a Genetic Algorithm that can address the fundamental problem of how one should weight the summary statistics included in an approximate Bayesian computation analysis built around an accept/reject algorithm, and how one might choose the tolerance for that analysis. We then demonstrate that using weighted statistics, and a well-chosen tolerance, in such an approximate Bayesian computation approach can result in improved performance, when compared to unweighted analyses, using one example drawn purely from statistics and two drawn from the estimation of population genetics parameters.
authors.affiliation: University of Southern California, USA.
authors.forename: Hsuan
authors.initials: H
authors.lastname: Jung
authors.forename: Paul
authors.initials: P
authors.lastname: Marjoram
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005828:Genetics, Population / Q000379:methods; D006239:Haplotypes; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D011995:Recombination, Genetic; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software