pith. sign in

arxiv: 1802.09720 · v1 · pith:JKWXO2VYnew · submitted 2018-02-27 · 📊 stat.CO · stat.ME· stat.ML

Overview of Approximate Bayesian Computation

S. A. Sisson , Y. Fan , M. A. Beaumont This is my paper
classification 📊 stat.CO stat.MEstat.ML
keywords approximatebayesiancomputationchapteroverviewappearbehindconcepts
0
0 comments X
read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Bring the noise: exact inference from noisy simulations in collider physics

    hep-ph 2025-02 unverdicted novelty 6.0

    Introduces pseudo-marginal MCMC with unbiased Poisson likelihood estimator for exact inference despite noisy collider Monte Carlo simulations.

  2. Machine Learning Techniques for Astrophysics and Cosmology: Simulation-Based Inference

    astro-ph.CO 2026-05 unverdicted novelty 2.0

    Simulation-based inference uses neural networks trained on simulations to enable parameter inference in cosmology and astrophysics where traditional likelihood calculations are intractable.