abc: an R package for approximate Bayesian computation (ABC)
By Katalin Csill'ery, Olivier Francois, Michael G. B. Blum. 2011
693 citations recorded by the source corpus.
1. Many recent statistical applications involve inference under complex models, where it is computationally prohibitive to calculate likelihoods but possible to simulate data. Approximate Bayesian computation (ABC) is devoted to these complex models because it bypasses the evaluation of the likelihood function by comparing observed and simulated data.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/273dbccba363e58600911f947bc5b67c7a318ada
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Taylor Pospisil
- Ann B. Lee
- Rafael Izbicki
- Scott Anthony Sisson
- Dennis Prangle
- Michael G. B. Blum
- Matthew A. Nunes
- Michael U Gutmann
In Statistics and Probability
- Taylor Pospisil
- Ann B. Lee
- Dennis Prangle
- Rafael Izbicki
- Scott Anthony Sisson
- Michael G. B. Blum
- Matthew A. Nunes
- MARK A. Beaumont
Citations in the corpus
- A comparative review of dimension reduction methods in approximate Bayesian computation (2012, methodology): “Both of these methods are now implemented in the freely available R package abc (Csilléry et al. 2012).”
- ABC–CDE: Toward Approximate Bayesian Computation With Complex High-Dimensional Data and Limited Simulations (2018, methodology): “We note that a related cross-validation procedure to choose the tolerance level in ABC has been proposed by Csillery, Francois, and Blum (2012), albeit using a loss function, that is, appropriate for point estimation only.”