TL;DR
This chapter provides an overview of ABC sampling algorithms, including rejection, importance sampling, MCMC, and sequential Monte Carlo methods, for approximating Bayesian posterior distributions.
Contribution
It compiles and explains the main ABC sampling algorithms, serving as a comprehensive guide for practitioners and researchers in the field.
Findings
Summarizes key ABC sampling algorithms
Provides algorithmic details and comparisons
Serves as a reference for ABC methods
Abstract
This Chapter, "ABC Samplers", is to appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and algorithms used to sample from the ABC approximation to the posterior distribution, including methods based on rejection/importance sampling, MCMC and sequential Monte Carlo.
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Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Bayesian Methods and Mixture Models · Statistical Methods and Bayesian Inference