Automating approximate Bayesian computation by local linear regression
Open Access
- 7 July 2009
- journal article
- Published by Springer Nature in BMC Genomic Data
- Vol. 10 (1) , 35
- https://doi.org/10.1186/1471-2156-10-35
Abstract
In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method.Keywords
This publication has 35 references indexed in Scilit:
- Approximately Sufficient Statistics and Bayesian ComputationStatistical Applications in Genetics and Molecular Biology, 2008
- Compound Tests for the Detection of Hitchhiking Under Positive SelectionMolecular Biology and Evolution, 2007
- Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population geneticsProceedings of the National Academy of Sciences, 2007
- Sequential Monte Carlo without likelihoodsProceedings of the National Academy of Sciences, 2007
- Controlling the False-Positive Rate in Multilocus Genome Scans for SelectionGenetics, 2007
- Approximate Bayesian Inference Reveals Evidence for a Recent, Severe Bottleneck in a Netherlands Population of Drosophila melanogasterGenetics, 2006
- Recombination and the Properties of Tajima's D in the Context of Approximate-Likelihood CalculationGenetics, 2005
- Multilocus patterns of nucleotide variability and the demographic and selection history of Drosophila melanogaster populationsGenome Research, 2005
- Population Genetics of Polymorphism and Divergence for Diploid Selection Models With Arbitrary DominanceGenetics, 2004
- Sampling theory for neutral alleles in a varying environmentPhilosophical Transactions Of The Royal Society B-Biological Sciences, 1994