Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods

@article{Mau1999BayesianPI,
  title={Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods},
  author={Bob Mau and Michael A. Newton and Bret R. Larget},
  journal={Biometrics},
  year={1999},
  volume={55}
}
Summary. We derive a Markov chain to sample from the posterior distribution for a phylogenetic tree given sequence information from the corresponding set of organisms, a stochastic model for these data, and a prior distribution on the space of trees. A transformation of the tree into a canonical cophenetic matrix form suggests a simple and effective proposal distribution for selecting candidate trees close to the current tree in the chain. We illustrate the algorithm with restriction site data… 
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A Bayesian method based on Markov chain simulation to study the phylogenetic relationship in a group of DNA sequences that strikes a reasonable balance between the desire to move globally through the space of phylogenies and the need to make computationally feasible moves in areas of high probability.
Phylogenetic Inference for Binary Data on Dendograms Using Markov Chain Monte Carlo
Abstract Using a stochastic model for the evolution of discrete characters among a group of organisms, we derive a Markov chain that simulates a Bayesian posterior distribution on the space of
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SUMMARY A large deviation result is established for the bootstrap empirical distribution in a finite sample space, thereby validating both nonparametric and parametric bootstrapping in certain
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