title: The evolutionary forest algorithm.
abstract: BACKGROUND
  Gene genealogies offer a powerful context for inferences about the evolutionary process based on presently segregating DNA variation. In many cases, it is the distribution of population parameters, marginalized over the effectively infinite-dimensional tree space, that is of interest. Our evolutionary forest (EF) algorithm uses Monte Carlo methods to generate posterior distributions of population parameters. A novel feature is the updating of parameter values based on a probability measure defined on an ensemble of histories (a forest of genealogies), rather than a single tree.
  
  
  RESULTS
  The EF algorithm generates samples from the correct marginal distribution of population parameters. Applied to actual data from closely related fruit fly species, it rapidly converged to posterior distributions that closely approximated the exact posteriors generated through massive computational effort. Applied to simulated data, it generated credible intervals that covered the actual parameter values in accordance with the nominal probabilities.
  
  
  BACKGROUND
  A C++ implementation of this method is freely accessible at http://www.isds.duke.edu/~scl13
authors.affiliation: Institute of Statistics and Decision Sciences, Duke University, Durham, NC, USA. scotland@stat.duke.edu
authors.forename: Scotland C
authors.initials: SC
authors.lastname: Leman
authors.forename: Marcy K
authors.initials: MK
authors.lastname: Uyenoyama
authors.forename: Michael
authors.initials: M
authors.lastname: Lavine
authors.forename: Yuguo
authors.initials: Y
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D002874:Chromosome Mapping / Q000379:methods*; D004252:DNA Mutational Analysis / Q000379:methods*; D019143:Evolution, Molecular; D014644:Genetic Variation / Q000235:genetics; D005828:Genetics, Population; D017422:Sequence Analysis, DNA / Q000379:methods*