ArticleProceedings of the National Academy of Sciences of the United States of America2009
Model criticism based on likelihood-free inference, with an application to protein network evolution.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
- On predictive inference for intractable models via approximate Bayesian computation.Statistics and computing · 2023Article
- Inference of brain networks with approximate Bayesian computation - assessing face validity with an example application in Parkinsonism.NeuroImage · 2021Article
- Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation.PLoS computational biology · 2020Article
- Flexible model selection for mechanistic network models.Journal of complex networks · 2020Article
- A nonparametric significance test for sampled networks.Bioinformatics (Oxford, England) · 2018Article
- A hierarchical Bayesian model for understanding the spatiotemporal dynamics of the intestinal epithelium.PLoS computational biology · 2017Article
- Bacterial protein meta-interactomes predict cross-species interactions and protein function.BMC bioinformatics · 2017Article
- Fundamentals and Recent Developments in Approximate Bayesian Computation.Systematic biology · 2017Article
- AABC: approximate approximate Bayesian computation for inference in population-genetic models.Theoretical population biology · 2015Article
- Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation.Heredity · 2014Article
- Back to BaySICS: a user-friendly program for Bayesian Statistical Inference from Coalescent Simulations.PloS one · 2014Article
- Approximate Bayesian inference for complex ecosystems.F1000prime reports · 2014Review
- Inferring population-level contact heterogeneity from common epidemic data.Journal of the Royal Society, Interface · 2013Article
- Approximate Bayesian computation.PLoS computational biology · 2013Article
- Graph spectral analysis of protein interaction network evolution.Journal of the Royal Society, Interface · 2012Article
- Population divergence with or without admixture: selecting models using an ABC approach.Heredity · 2012Article
- Phylodynamic inference and model assessment with approximate bayesian computation: influenza as a case study.PLoS computational biology · 2012Article
- Choice of summary statistic weights in approximate Bayesian computation.Statistical applications in genetics and molecular biology · 2011Article
- Lack of confidence in approximate Bayesian computation model choice.Proceedings of the National Academy of Sciences of the United States of America · 2011Article
- Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v1.0).BMC bioinformatics · 2010Article
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4 authors.
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Abstract
Mathematical models are an important tool to explain and comprehend complex phenomena, and unparalleled computational advances enable us to easily explore them without any or little understanding of their global properties. In fact, the likelihood of the data under complex stochastic models is often analytically or numerically intractable in many areas of sciences. This makes it even more important to simultaneously investigate the adequacy of these models-in absolute terms, against the data, rather than relative to the performance of other models-but no such procedure has been formally discussed when the likelihood is intractable. We provide a statistical interpretation to current developments in likelihood-free Bayesian inference that explicitly accounts for discrepancies between the model and the data, termed Approximate Bayesian Computation under model uncertainty (ABCmicro). We augment the likelihood of the data with unknown error terms that correspond to freely chosen checking functions, and provide Monte Carlo strategies for sampling from the associated joint posterior distribution without the need of evaluating the likelihood. We discuss the benefit of incorporating model diagnostics within an ABC framework, and demonstrate how this method diagnoses model mismatch and guides model refinement by contrasting three qualitative models of protein network evolution to the protein interaction datasets of Helicobacter pylori and Treponema pallidum. Our results make a number of model deficiencies explicit, and suggest that the T. pallidum network topology is inconsistent with evolution dominated by link turnover or lateral gene transfer alone.
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