Journal
ANNUAL REVIEW OF ECOLOGY, EVOLUTION, AND SYSTEMATICS, VOL 41
Volume 41, Issue -, Pages 379-406Publisher
ANNUAL REVIEWS
DOI: 10.1146/annurev-ecolsys-102209-144621
Keywords
Bayesian inference; Monte Carlo; simulation
Categories
Ask authors/readers for more resources
In the past 10 years a statistical technique, approximate Bayesian computation (ABC), has been developed that can be used to infer parameters and choose between models in the complicated scenarios that are often considered in the environmental sciences For example, based on gene sequence and microsatellite clan, the method has been used to choose between competing models of human demographic history as well as to infer growth rates, times of divergence, and other parameters The method fits naturally in the Bayesian inferential framework, and a brief overview is given of the key concepts Three main approaches to ABC have been developed, and these are described and compared Although the method arose in population genetics, ABC is increasingly used in other fields, including epidemiology, systems biology, ecology, and agent-based modeling, and many of these applications ire briefly described
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available