Inferring coalescence times from DNA sequence data.
By S. Tavaré, David J. Balding, Robert C. Griffiths, Peter Donnelly. 1997
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Paul Fearnhead
- Laurent Excoffier
- Michael U Gutmann
- Joeri Hermans
- Jukka Corander
- Owen Thomas
- MARK A. Beaumont
- Amos J. Storkey
Citations in the corpus
- GLUE: 20 years on (2014, methodology): “…Bayesian computation (ABC) was given to a technique (actually somewhat analogous to a form of GLUE) developed for evaluating models in genetics for cases where a suitable formal Bayes likelihood function is difficult to define or evaluate (e.g. Tavaré et al., 1997; Beaumont et al., 2002).”
- Statistical properties of new neutrality tests against population growth. (2002, methodology): “To check that effect on the R2 we performed a prospective analysis generating samples conditional on u and S using the rejection algorithm of Tavaré et al. (1997).”
- Statistical inference for stochastic simulation models--theory and application. (2011, background): “Recently, however, it was realized that a simpler nonparametric approximation can be combined very efficiently with the sampling techniques that are discussed in the next section (Tavare et al. 1997; Marjoram et al. 2003; Sisson et al. 2007).”
- MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity (2011, methodology): “MTML-msBayes implements hierarchical ABC by way of a standard rejection/acceptance algorithm [10,26-30] followed by an optional transformation step.”
- A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation (2012, methodology): “Although the origin of ABC is generally assigned to Fu and Li (1997), Tavaré et al. (1997) and Pritchard 86 et al. (1999), some aspects, such as the summary description of the full data, inference for implicit stochastic models and algorithms directly sampling from the posterior distribution 88…”
- COMPUTER PROGRAMS: onesamp: a program to estimate effective population size using approximate Bayesian computation (2008, unknown): “To approximate this, the simulated candidate value Φi and associated Si are accepted when the Euclidean distance || Si – S*|| < g, where g defines a distance such that a proportion dg of points closest to S* are accepted (Tavaré et al. 1997).”
- Lack of confidence in approximate Bayesian computation model choice (2011, background): “(U [1, 10]) and the time of the first divergence (U [50, 500]).”
- abc: an R package for approximate Bayesian computation (ABC) (2011, background): “The name ABC was first coined by Beaumont et al. (2002) in population genetics, for inference under coalescent models, but its origin goes back to works by Tavaré et al. (1997); Pritchard et al. (1999).”
- Likelihood-Free Inference by Ratio Estimation (2016, methodology): “They are collectively known as likelihood-free inference methods, and include approximate Bayesian computation (Tavar´e et al., 1997; Pritchard et al., 1999; Beaumont et al., 2002) and the synthetic likelihood approach (Wood, 2010).”
- Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation (2008, methodology): “Consequently, a line of research including the works of Tavaré et al. (1997), Weiss and von Haeseler (1998), Pritchard et al. (1999) and Marjoram et al. (2003) developed a new approach termed Approximate Bayesian Computation (or ABC) by Beaumont et al. (2002).”
- Bayesian computation: a summary of the current state, and samples backwards and forwards (2015, methodology): “Although it has now diffused into a wide range of applications, the technique of ABC was first introduced by and for population genetics (Tavaré et al. 1997; Pritchard et al. 1999) to handle ancestry models driven by Kingman’s coalescent and with strictly intractable likelihoods (Beaumont 2010).”
- Robust generalised Bayesian inference for intractable likelihoods (2021, unknown): “…Bayesian synthetic likelihood ; Beaumont et al., 2002; Cherief-Abdellatif & Alquier, 2020; Frazier, 2020; Marin et al., 2012; Price et al., 2018; Tavaré et al., 1997), (2) auxiliary variable MCMC (such as the exchange algorithm and pseudo-marginal MCMC; Andrieu & Roberts, 2009; M ⊘ ller et al.,…”