ArticleProceedings of the National Academy of Sciences of the United States of America2011
Lack of confidence in approximate Bayesian computation model choice.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.
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84 citing papers in PubMed.
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- Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation.PLoS computational biology · 2022Article
- Congruent evolutionary responses of European steppe biota to late Quaternary climate change.Nature communications · 2022Article
- A flexible Bayesian framework for unbiased estimation of timescales.Nature computational science · 2022Article
- Optimal Bayesian design for model discrimination via classification.Statistics and computing · 2022Article
- A Probabilistic Model for Indel Evolution: Differentiating Insertions from Deletions.Molecular biology and evolution · 2021Article
- Informing conservation strategies with museum genomics: Long-term effects of past anthropogenic persecution on the elusive European wildcat.Ecology and evolution · 2021Article
- Inference of brain networks with approximate Bayesian computation - assessing face validity with an example application in Parkinsonism.NeuroImage · 2021Article
- ABCDP: Approximate Bayesian Computation with Differential Privacy.Entropy (Basel, Switzerland) · 2021Article
- Climate-induced range shifts shaped the present and threaten the future genetic variability of a marine brown alga in the Northwest Pacific.Evolutionary applications · 2021Article
- Cellular connectomes as arbiters of local circuit models in the cerebral cortex.Nature communications · 2021Article
- Genetic consequences of being a dwarf: do evolutionary changes in life-history traits influence gene flow patterns in populations of the world's smallest goldenrod?Annals of botany · 2020Article
- Flexible model selection for mechanistic network models.Journal of complex networks · 2020Article
24 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Approximate Bayesian computation (ABC) have become an essential tool for the analysis of complex stochastic models. Grelaud et al. [(2009) Bayesian Anal 3:427-442] advocated the use of ABC for model choice in the specific case of Gibbs random fields, relying on an intermodel sufficiency property to show that the approximation was legitimate. We implemented ABC model choice in a wide range of phylogenetic models in the Do It Yourself-ABC (DIY-ABC) software [Cornuet et al. (2008) Bioinformatics 24:2713-2719]. We now present arguments as to why the theoretical arguments for ABC model choice are missing, because the algorithm involves an unknown loss of information induced by the use of insufficient summary statistics. The approximation error of the posterior probabilities of the models under comparison may thus be unrelated with the computational effort spent in running an ABC algorithm. We then conclude that additional empirical verifications of the performances of the ABC procedure as those available in DIY-ABC are necessary to conduct model choice.
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