Comparison of Bayesian and maximum-likelihood inference of population genetic parameters

By Peter Beerli. Bioinform., 2006

898 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2761570cce67bc85fa8db367ac032e8e5020aefb

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In Genetics

Citations in the corpus

BEAST 2: A Software Platform for Bayesian Evolutionary Analysis (2014, result): “This capability is similar to that of MIGRATE [28–30] and is absent from the original BEAST implementation.”
Phylogeography of Quercus aquifolioides provides novel insights into the Neogene history of a major global hotspot of plant diversity in south‐west China (2017, methodology): “2) was assessed using the software Migrate- n (Beerli, 2006).”
Inferring dispersal: a Bayesian approach to phylogeny‐based island biogeography, with special reference to the Canary Islands (2008, methodology): “For example, Peter Beerli’s MIGRATE (Beerli, 2006) uses ML or Bayesian MCMC to estimate effective population sizes and past migration rates between n island populations by assuming migration matrix models of varying complexity (e.”
The role of molecular genetics in sculpting the future of integrative biogeography (2008, methodology): “Although continuing development of methods based on coalescent theory (Beaumont, 2004; Anderson et al., 2005; Beerli, 2006; Carstens and Richards, 2007; Edwards et al., 2007; Hey and Nielsen, 2007; Hickerson et al., 2007; Knowles and Carstens, 2007) and niche-modelling (above citations) are reinvigo”
Prospects and challenges for parametric models in historical biogeographical inference (2009, methodology): “Advancements in model-based phylogeographic methods are on the rise, notably through the blending of demographic parameter estimation (e.g. Beerli, 2006) with explicit biogeographical hypothesis testing (Knowles & Maddison, 2002) in combination with ecological niche modelling (see Richards et al.,…”
Larval dispersal and marine population connectivity. (2009, background): “…of these techniques, point to promising new approaches (e.g., Monte Carlo, maximum likelihood, and Bayesian analyses; Neilsen & Wakeley 2001, Manel et al. 2005, Beerli 2006), and note important limitations of using genetic markers to differentiate population structure at various spatial scales.”
IS A NEW AND GENERAL THEORY OF MOLECULAR SYSTEMATICS EMERGING? (2009, methodology): “Falush, D., M. Stephens, and J. K. Pritchard.”
Computer programs for population genetics data analysis: a survival guide (2006, background): “, which facilitates the incorporation of some prior knowledge on the parameters of interest, often leading to improved estimations (for an example, see Ref”
How to use MIGRATE or why are Markov chain Monte Carlo programs difficult to use (2009, methodology): “Recently, the programs MIGRATE (Beerli 2006) and LAMARC (Kuhner 2006) were improved and can run both BI and ML.”
What can genetics tell us about population connectivity? (2010, background): “However, it is possible that the poor performance observed by Abdo et al. (2004) resulted from lack of convergence of the Markov chain Monte Carlo (MCMC) algorithm in their analyses (Beerli 2006).”
Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7 (2018, methodology): “2014), LAMARC (Kuhner 2006), Migrate (Beerli 2006), MrBayes (Ronquist et al.”
Footprints on water: the genetic wake of dispersal among reefs (2007, methodology): “The most recent version uses a Bayesian framework, which is faster and more accurate ( Beerli 2006 ).”
Unified Framework to Evaluate Panmixia and Migration Direction Among Multiple Sampling Locations (2010, methodology): “In addition, the programming and the successful application of Bayesian inference programs are often simpler than maximum likelihood (Beerli 2006).”
Introgression of crop alleles into wild or weedy populations (2013, methodology): “Two widely used programs implementing these methods are MIGRATE (Beerli 2006) and the isolation with migration (IM) suite of programs (Hey 2010).”