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Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure
2015
Proceedings of the National Academy of Sciences of the United States of America
Admixture models are a ubiquitous approach to capture latent population structure in genetic samples. Despite the widespread application of admixture models, little thought has been devoted to the quality of the model fit or the accuracy of the estimates of parameters of interest for a particular study. Here we develop methods for validating admixture models based on posterior predictive checks (PPCs), a Bayesian method for assessing the quality of a statistical model. We develop PPCs for five
doi:10.1073/pnas.1412301112
pmid:26071445
pmcid:PMC4491772
fatcat:txix7krb4zfwlnmjo33tg4ngt4