Overview of Approximate Bayesian Computation
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Abstract (arXiv)
This Chapter, "Overview of Approximate Bayesian Computation", is to appear as the first chapter in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and concepts behind ABC methods with many examples and illustrations.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 0; 2025: 0; 2026: 2.
6 papers.
- 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Modelscited by 50
- 2023Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layerscited by 2
- 2021MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectoriescited by 1
- 2023Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inferencecited by 1
- 2026Simulation-Based Empirical Bayescited by 1
- 2026Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approachescited by 0
- 2026Simulation-Based Empirical Bayes23 Jul 2026
- 2026Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approaches28 May 2026
- 2023Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers3 Dec 2023
- 2023Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inference27 Jun 2023
- 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models10 Oct 2022
- 2021MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories3 Jun 2021