FUBAR: a fast, unconstrained bayesian approximation for inferring selection.

@article{Murrell2013FUBARAF,
  title={FUBAR: a fast, unconstrained bayesian approximation for inferring selection.},
  author={B. Murrell and Sasha Moola and Amandla Mabona and Thomas Weighill and Daniel J. Sheward and Sergei L. Kosakovsky Pond and Konrad Scheffler},
  journal={Molecular biology and evolution},
  year={2013},
  volume={30 5},
  pages={
          1196-205
        }
}
Model-based analyses of natural selection often categorize sites into a relatively small number of site classes. Forcing each site to belong to one of these classes places unrealistic constraints on the distribution of selection parameters, which can result in misleading inference due to model misspecification. We present an approximate hierarchical Bayesian method using a Markov chain Monte Carlo (MCMC) routine that ensures robustness against model misspecification by averaging over a large… 

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