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Introduction to Markov Chain Monte Carlo Methods in Molecular Evolution

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References

  1. J. P. Bielawski and Z. Yang. Maximum likelihood methods for detecting adaptive protein evolution. Chapter 5, this volume.

    Google Scholar 

  2. J. P. Bollback. Posterior mappings and posterior predictive distributions. Chapter 16, this volume.

    Google Scholar 

  3. B. P. Carlin and T. A. Louis. Bayes and Empirical Bayes Methods for Data Analysis. Chapman and Hall/CRC, Boca Raton, second edition, 2000.

    Google Scholar 

  4. M. Dimmic. Markov models of protein sequence evolution. Chapter 9, this volume.

    Google Scholar 

  5. R. Durrett. Genome rearrangement. Chapter 11, this volume.

    Google Scholar 

  6. J. Felsenstein. Inferring Phylogenies. Sinauer Associates, Inc., Sunderland, MA, 2004.

    Google Scholar 

  7. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis. Chapman and Hall/CRC, Boca Raton, 1995.

    Google Scholar 

  8. S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721–741, 1984.

    Article  Google Scholar 

  9. C. J. Geyer. Markov chain Monte Carlo maximum likelihood. In E. M. Kerimidas, editor, Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface, pages 156–163. Interface Foundation, Fairfax Station, VA, 1991.

    Google Scholar 

  10. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, editors. Markov Chain Monte Carlo in practice. Chapman and Hall/CRC, Boca Raton, 1996.

    Google Scholar 

  11. P. J. Green. Reversible jump MCMC computation and Bayesian model determination. Biometrika, 82:711–732, 1995.

    Article  MATH  MathSciNet  Google Scholar 

  12. W. K. Hastings. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57:97–109, 1970.

    Article  MATH  Google Scholar 

  13. D. Hitchcock. A history of the Metropolis-Hastings algorithm. The American Statistician, 57:254–257, 2003.

    Article  MathSciNet  Google Scholar 

  14. J. P. Huelsenbeck and F. Ronquist. Bayesian analysis of molecular evolution using MrBayes. Chapter 8, this volume.

    Google Scholar 

  15. J. P. Huelsenbeck and F. Ronquist. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics, 17:754–755, 2001.

    Article  PubMed  CAS  Google Scholar 

  16. J. P. Huelsenbeck, F. Ronquist, R. Nielsen, and J. P. Bollback. Bayesian inference of phylogeny and its impact on evolutionary biology. Science, 294:2310–2314, 2001.

    Article  PubMed  CAS  Google Scholar 

  17. B. Larget and D. L. Simon. Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees. Molecular Biology and Evolution, 16:750–759, 1999.

    CAS  Google Scholar 

  18. S. Li, H. Doss, and D. Pearl. Phylogenetic tree reconstruction using Markov chain Monte Carlo. Journal of the American Statistical Society, 95:493–508, 2000.

    Google Scholar 

  19. J. S. Liu. Monte Carlo Strategies in Scientific Computing. Springer, New York, 2001.

    Google Scholar 

  20. B. Mau and M. A. Newton. Phylogenetic inference for binary data on dendograms using Markov chain Monte Carlo. Journal of Computational and Graphical Statistics, 6:122–131, 1997.

    Article  Google Scholar 

  21. B. Mau, M. A. Newton, and B. Larget. Bayesian phylogenetic inference via Markov chain Monte Carlo methods. Biometrics, 55:1–12, 1999.

    Article  MathSciNet  PubMed  CAS  Google Scholar 

  22. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller. Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21:1087–1092, 1953.

    Article  CAS  Google Scholar 

  23. C. P. Robert and G. Casella. Monte Carlo Statistical Methods. Springer, New York, 2002.

    Google Scholar 

  24. D. Simon and B. Larget. Bayesian analysis in molecular biology and evolution (BAMBE). http://www.mathcs.duq.edu/larget/bambe.html, 2001.

    Google Scholar 

  25. J. L. Thorne and H. Kishino. Estimation of divergence times from molecular sequence data. Chapter 9, this volume.

    Google Scholar 

  26. L. Tierney. Markov chains for exploring posterior distributions (with discussion). Annals of Statistics, 22:1701–1762, 1994.

    Article  MATH  MathSciNet  Google Scholar 

  27. Z. Yang and B. Rannala. Bayesian phylogenetic inference using DNA sequences: A Markov chain Monte Carlo method. Molecular Biology and Evolution, 14:717–724, 1997.

    PubMed  CAS  Google Scholar 

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Larget, B. (2005). Introduction to Markov Chain Monte Carlo Methods in Molecular Evolution. In: Statistical Methods in Molecular Evolution. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/0-387-27733-1_3

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