Approximate Bayesian Computations to fit and compare insurance loss models

TL;DR

This paper applies Approximate Bayesian Computation (ABC) with sequential Monte Carlo and Wasserstein distance to fit and compare insurance loss models using aggregated data, providing a likelihood-free approach.

Contribution

The paper introduces a Python implementation of ABC with sequential Monte Carlo and Wasserstein distance for insurance loss model calibration and comparison.

Findings

Effective model fitting and comparison using ABC.

Python implementation demonstrates practical applicability.

Likelihood-free approach simplifies complex model calibration.

Abstract

Approximate Bayesian Computation (ABC) is a statistical learning technique to calibrate and select models by comparing observed data to simulated data. This technique bypasses the use of the likelihood and requires only the ability to generate synthetic data from the models of interest. We apply ABC to fit and compare insurance loss models using aggregated data. A state-of-the-art ABC implementation in Python is proposed. It uses sequential Monte Carlo to sample from the posterior distribution and the Wasserstein distance to compare the observed and synthetic data.