A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation

Simon Aeschbacher, Mark A. Beaumont, Andreas Futschik

Research output: Contribution to journalArticle (Academic Journal)peer-review

73 Citations (Scopus)


The choice of summary statistics is a crucial step in approximate Bayesian computation (ABC). Since statistics are often not sufficient, this choice involves a trade-off between loss of information and reduction of dimensionality. The latter may increase the efficiency of ABC. Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine-learning literature. We consider different types of boosting and compare them to partial least-squares regression as an alternative. To mitigate the lack of sufficiency, we also propose an approach for choosing summary statistics locally, in the putative neighborhood of the true parameter value. We study a demographic model motivated by the reintroduction of Alpine ibex (Capra ibex) into the Swiss Alps. The parameters of interest are the mean and standard deviation across microsatellites of the scaled ancestral mutation rate (theta(anc) = 4N(e)u) and the proportion of males obtaining access to matings per breeding season (omega). By simulation, we assess the properties of the posterior distribution obtained with the various methods. According to our criteria, ABC with summary statistics chosen locally via boosting with the L-2-loss performs best. Applying that method to the ibex data, we estimate (theta) over cap (anc) approximate to 1: 288 and find that most of the variation across loci of the ancestral mutation rate u is between 7.7 x 10(-4) and 3.5 x 10(-3) per locus per generation. The proportion of males with access to matings is estimated as (omega) over cap approximate to 0.21, which is in good agreement with recent independent estimates.

Original languageEnglish
Pages (from-to)1027-1047
Number of pages21
Issue number3
Publication statusPublished - Nov 2012


  • Animals
  • Computer Simulation
  • Reproducibility of Results
  • Genetics, Population
  • Breeding
  • Models, Genetic
  • Bayes Theorem
  • Algorithms
  • Switzerland
  • Male
  • Mutation Rate


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