# abc: an R package for approximate Bayesian computation (ABC)

@article{Csillery2011abcAR, title={abc: an R package for approximate Bayesian computation (ABC)}, author={Katalin Csill'ery and Olivier François and Michael G. B. Blum}, journal={Methods in Ecology and Evolution}, year={2011}, volume={3} }

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.

## 557 Citations

### A new approach to choose acceptance cutoff for approximate Bayesian computation

- Computer Science
- 2013

The development of an algorithm to choose the tolerance level for ABC is reported, illustrated by simulating the estimation of scaled mutation and recombination rates and showing that the proposed algorithm performs well.

### abctools: An R Package for Tuning Approximate Bayesian Computation Analyses

- Computer ScienceR J.
- 2015

A new software package for R, abctools, is presented which provides methods for tuning ABC algorithms, including recent dimension reduction algorithms to tune the choice of summary statistics, and coverage methods to tuneThe choice of threshold.

### Approximate Bayesian Computation

- Computer SciencePLoS Comput. Biol.
- 2013

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics that widen the realm of models for which statistical inference can be considered and exacerbates the challenges of parameter estimation and model selection.

### Calibration and evaluation of individual-based models using Approximate Bayesian Computation

- Computer Science
- 2015

### Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation

- Computer Science
- 2012

This work shows how to construct appropriate summary statistics for ABC in a semi‐automatic manner, and shows that optimal summary statistics are the posterior means of the parameters.

### BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood

- Computer Science, MathematicsJ. Stat. Softw.
- 2022

An R package called BSL is presented that amalgamates the aforementioned methods and more into a single, easy-to-use and coherent piece of software.

### EasyABC: performing efficient approximate Bayesian computation sampling schemes using R

- Computer Science
- 2013

This work introduces the R package ‘EasyABC’ that enables one to launch a series of simulations from the R platform and to retrieve the simulation outputs in an appropriate format for post‐processing, and implements several efficient parameter sampling schemes to speed up the ABC procedure.

### A Guide to General-Purpose Approximate Bayesian Computation Software

- Computer Science
- 2018

This Chapter presents general-purpose software to perform Approximate Bayesian Computation (ABC) as implemented in the R-packages abc and EasyABC and the c++ program ABCtoolbox and demonstrates how to combine ABC with Markov Chain Monte Carlo and describe a realistic population genetics application.

### Deviance Information Criteria for Model Selection in Approximate Bayesian Computation

- Biology
- 2011

This work proposes novel approaches to model selection based on posterior predictive distributions and approximations of the deviance that can settle some contradictions between the computation of model probabilities and posterior predictive checks using ABC posterior distributions.

### An Approximate Likelihood Perspective on ABC Methods

- Computer Science
- 2017

This article provides a unifying review, general representation, and classification of all ABC methods from the view of approximate likelihood theory, which clarifies how ABC methods can be characterized, related, combined, improved, and applied for future research.

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This work proposes novel approaches to model selection based on posterior predictive distributions and approximations of the deviance that can settle some contradictions between the computation of model probabilities and posterior predictive checks using ABC posterior distributions.

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