A Guide to General-Purpose Approximate Bayesian Computation Software
Athanasios Kousathanas, Pablo Duchen, Daniel Wegmann
TL;DR
This paper reviews general-purpose software tools for Approximate Bayesian Computation, demonstrating their use in parameter inference, model selection, and validation across various models and applications.
Contribution
It provides a comprehensive guide to existing ABC software packages, illustrating their application with examples and a real-world population genetics case study.
Findings
Effective parameter inference and model selection demonstrated
Integration of ABC with MCMC enhances inference capabilities
Application to population genetics showcases practical utility
Abstract
This Chapter, "A Guide to General-Purpose ABC Software", is to appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). We present general-purpose software to perform Approximate Bayesian Computation (ABC) as implemented in the R-packages abc and EasyABC and the c++ program ABCtoolbox. With simple toy models we demonstrate how to perform parameter inference, model selection, validation and optimal choice of summary statistics. We demonstrate how to combine ABC with Markov Chain Monte Carlo and describe a realistic population genetics application.
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Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Stochastic processes and statistical mechanics · Gaussian Processes and Bayesian Inference