2013
DOI: 10.1111/evo.12241
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Recommendations for Using Msbayes to Incorporate Uncertainty in Selecting an Abc Model Prior: A Response to Oaks Et Al.

Abstract: Approximate Bayesian computation (ABC) techniques have seen rapid and accelerating development in biology, with applications including population genetics, systems biology, and community ecology (reviewed in Beaumont 2010;Csilléry et al. 2010). However, the approximations and model assumptions inherent in ABC can make model choice and parameter estimation problematic, and careful simulation-based validation and assessment of posterior predictive power are required (Gelman et al.

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Cited by 31 publications
(161 citation statements)
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References 51 publications
(131 reference statements)
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“…; Hickerson et al . ). Thirty species were analysed in the comparison between El Copé and Brewster, and fifteen species were compared between Cana and Brewster (Fig.…”
Section: Methodsmentioning
confidence: 97%
“…; Hickerson et al . ). Thirty species were analysed in the comparison between El Copé and Brewster, and fifteen species were compared between Cana and Brewster (Fig.…”
Section: Methodsmentioning
confidence: 97%
“…As a goodness-of-fit check to ascertain whether the model and our chosen priors can produce the main features of the observed data and to check for prior sampling efficiency (Hickerson et al 2014), we deployed a PCA on prior and posterior samples of the 16 summary statistics. Initially, we used the first two principal components of the summary statistics calculated from 1,000 random draws from the simulated prior distribution and 1,000 samples from the hABC posterior to compare with these first two components from the 32 avian population samples ( D* ).…”
Section: Methodsmentioning
confidence: 99%
“…Consider the case in which initial hABC analyses of 22 taxon pairs spanning five orders of terrestrial vertebrates sampled from six island pairs across the Philippine archipelago fit a model of simultaneous divergence, seemingly providing support for the "species-pump" diversification hypothesis (35). However, when the authors performed a suite of simulation-based power analyses and discovered inherent bias in inferences because of improper priors on demographic and divergence-time parameters in the msBayes program (69,70), reanalysis showed divergence times were not clustered. The merit of identifying methodological biases not withstanding, equivalent scrutiny should be given to the metric used to evaluate support for the species-pump diversification hypothesis, that is, the concordance criterion.…”
Section: Insights Gained From Discordance Highlight the Promise Of A mentioning
confidence: 99%