ArticleStatistical applications in genetics and molecular biology2011
Choice of summary statistic weights in approximate Bayesian computation.
Article in Statistical applications in genetics and molecular biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An automatic adaptive method to combine summary statistics in approximate Bayesian computation.PloS one · 2020Article
- Early mutation bursts in colorectal tumors.PloS one · 2017Article
- Ancestral inference in tumors: how much can we know?Journal of theoretical biology · 2014Article
- Approximate Bayesian inference for complex ecosystems.F1000prime reports · 2014Review
- Review
- Approximation Bayesian Computation.OA genetics · 2013Article
- A novel approach for choosing summary statistics in approximate Bayesian computation.Genetics · 2012Article
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2 authors.
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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.
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