Approximate Bayesian Computation in Evolution and Ecology

By MARK A. Beaumont. 2010

570 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Mathematical Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9869df823c3bb68a0164a67d60475981dba17cfd

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Citations in the corpus

DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data (2014, background): “General statistical features, practical aspects and applications of ABC in evolutionary biology have been reviewed in three recent papers (Beaumont, 2010; Bertorelle et al., 2010; Csille´ry et al., 2010).”
ABC–CDE: Toward Approximate Bayesian Computation With Complex High-Dimensional Data and Limited Simulations (2018, methodology): “1 Introduction For many statistical inference problems in the sciences the relationship between the parameters of interest and observable data is complicated, but it is possible to simulate realistic data according to some model; see Beaumont (2010); Estoup et al.”
abc: an R package for approximate Bayesian computation (ABC) (2011, methodology): “ABC was first proposed in population genetics to do inference under coalescent models [1, 2], but it is now increasingly applied in other fields, such as ecology or systems biology (see [3–5] for reviews of ABC methods and applications).”
Introgression of crop alleles into wild or weedy populations (2013, methodology): “Table 1 features a nonexhaustive list of crop species for which introgression is known to have occurred or is occurring on the basis of substantial data.”
The data deluge (2015, methodology): “Moreover, in addition to standard Bayesian methods, there is now also a range of further related approaches to data uncertainty (Beaumont 2010; Crema 2012; Bevan et al .”
RangeShifter: a platform for modelling spatial eco‐evolutionary dynamics and species' responses to environmental changes (2014, methodology): “A promising avenue could be enhancing RangeShifter such that it can be used with approximate Bayesian computation methods (Beaumont 2010; Hartig et al. 2011).”