Inference of demographic history from genealogical trees using reversible jump Markov chain Monte Carlo

@article{OpgenRhein2004InferenceOD,
  title={Inference of demographic history from genealogical trees using reversible jump Markov chain Monte Carlo},
  author={Rainer Opgen-Rhein and Ludwig Fahrmeir and Korbinian Strimmer},
  journal={BMC Evolutionary Biology},
  year={2004},
  volume={5},
  pages={6 - 6}
}
BackgroundCoalescent theory is a general framework to model genetic variation in a population. Specifically, it allows inference about population parameters from sampled DNA sequences. However, most currently employed variants of coalescent theory only consider very simple demographic scenarios of population size changes, such as exponential growth.ResultsHere we develop a coalescent approach that allows Bayesian non-parametric estimation of the demographic history using genealogies… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 33 REFERENCES

Exploring the demographic history of DNA sequences using the generalized skyline plot.

VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

Genetic epidemiology of hepatitis C virus throughout egypt.

VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

Exponential Spread of Hepatitis C Virus Genotype 4a in Egypt

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Inferring Phylogenies Sunderland, MA: Sinauer Associates

  • J Felsenstein
  • 2004
VIEW 1 EXCERPT