ABCtoolbox: a versatile toolkit for approximate Bayesian computations

Background: The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC) algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations. Results: Here we present ABCtoolbox, a series of open source programs to perform Approximate Bayesian Computations (ABC). It implements various ABC algorithms including rejection sampling, MCMC without likelihood, a Particle-based sampler and ABC-GLM. ABCtoolbox is bundled with, but not limited to, a program that allows parameter inference in a population genetics context and the simultaneous use of different types of markers with different ploidy levels. In addition, ABC...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Daniel Wegmann, Christoph Leuenberger, Samuel Neuenschwander, Laurent Excoffier
• Keywords: Approximate Bayesian Computations, BMCBI 2010, Computation, Parameter