EDBT 2026 Demo / reviewers in the wild / expert
Thomas Guillemaud
dblp:74/7065
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2008
0000-0003-0451-1644ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
approximate bayesian computation |
0.1 | 1 | 2008 | Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation · Bioinform. 2008 |
Bioinformatics and computational biology › population genetics › population parameter estimation
demographic inference |
0.1 | 1 | 2008 | Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation · Bioinform. 2008 |
Bioinformatics and computational biology
population genetics |
0.1 | 1 | 2008 | Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation · Bioinform. 2008 |
Methods — techniques the papers use, named apart from their topics
parameter estimation · 0.1bias and precision estimation · 0.1approximate bayesian computation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computationabstractUNLABELLED: Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC. AVAILABILITY: The software DIY ABC is freely available at http://www.montpellier.inra.fr/CBGP/diyabc. Jean-Marie Cornuet, Mark A. Beaumont, Christian P. Robert, Jean-Michel Marin, David J. Balding, Thomas Guillemaud, Arnaud Estoup |
Bioinform. | 7 |