Overview of Approximate Bayesian Computation
arXiv:1802.09720
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.
References in corpus (11)
- MCMC using Hamiltonian dynamics
- Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
- Non-linear regression models for Approximate Bayesian Computation
- A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation
- Approximate Bayesian computation (ABC) gives exact results under the assumption of model error
- HIV with contact-tracing: a case study in Approximate Bayesian Computation
- Likelihood-free Markov chain Monte Carlo
- Bayesian Symbol Detection in Wireless Relay Networks via Likelihood-Free Inference
- Approximating the Likelihood in Approximate Bayesian Computation
- Divide and conquer in ABC: Expectation-Progagation algorithms for likelihood-free inference
- Approximate Bayesian Computation and Model Validation for Repulsive Spatial Point Processes
Cited by in corpus (6)
- Bayesian Stokes inversion with Normalizing flows
- Updated Bounds on Axion-Like Particles from X-ray Observations
- Scalable inference with Autoregressive Neural Ratio Estimation
- Likelihood-free approximate Gibbs sampling
- Recalibration: A post-processing method for approximate Bayesian computation
- Detecting conflicting summary statistics in likelihood-free inference