Molecular Evolution A Statistical Approach
By Lena Osterhagen. 2016
307 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/467de68193923e78b4d5f3faafcd7d6afc36b3f0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Xiaofan Zhou
- Xing‐Xing Shen
- C. T. Hittinger
- K. Angelis
- Mario dos Reis
- Liam J. Revell
- Antonis Rokas
- J. Clavel
Citations in the corpus
- Unguided Species Delimitation Using DNA Sequence Data from Multiple Loci (2014, methodology): “The Bayesian species-delimitation method of Yang and Rannala (2010) and Rannala and Yang (2013) has a number of advantages over heuristic methods (Fujita et al. 2012; Yang 2014).”
- The BPP program for species tree estimation and species delimitation (2015, methodology): “P)/(NE), where P is the true posterior model probability, and E is the efficiency of the MCMC sample (see, e.g., Yang, 2014, p.214).”
- The impact of ancestral population size and incomplete lineage sorting on Bayesian estimation of species divergence times (2015, background): “…(Takahata et al., 1995; Edwards and Beerli, 2000; Kubatko and Degnan, 2007; Knowles and Kubatko, 2010; Burbrink and Pyron, 2011; Oliver, 2013; Yang, 2014), a majority of molecular clock dating analyses are still carried out ignoring the effects of ancestral population size and incomplete…”
- phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things) (2023, methodology): “…with two additional parameters to be estimated! phytools now makes it very easy to undertake joint or marginal ancestral state reconstruction (e.g., Yang 2014; Revell and Harmon 2022) under a hidden-rate model, as well as under other models we’ve seen in this article (such as the standard extended…”
- Evaluating Fast Maximum Likelihood-Based Phylogenetic Programs Using Empirical Phylogenomic Data Sets (2017, background): “…identification of the tree best representing the evolutionary history of the underlying data—is of fundamental importance to many biological disciplines, including but not limited to systematics, molecular evolution, and comparative genomics (Felsenstein 2003; Xia 2013; Hamilton 2014; Yang 2014).”