# Modelling extremes using approximate Bayesian Computation

arXiv: https://arxiv.org/abs/1411.1451 · HTML: https://mlsanity.com/paper/1411.1451/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2014.

## Built on by (most cited first, 2 of 2)

- [Fast parameter estimation of Generalized Extreme Value distribution using Neural Networks](https://mlsanity.com/paper/2305.04341/) (2023)
- [An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation](https://mlsanity.com/paper/2311.12530/) (2023)

## Abstract (arXiv)

By the nature of their construction, many statistical models for extremes result in likelihood functions that are computationally prohibitive to evaluate. This is consequently problematic for the purposes of likelihood-based inference. With a focus on the Bayesian framework, this chapter examines the use of approximate Bayesian computation (ABC) techniques for the fitting and analysis of statistical models for extremes. After introducing the ideas behind ABC algorithms and methods, we demonstrate their application to extremal models in stereology and spatial extremes.