On the approximation of posterior laws in compound loss models by conditional Wasserstein GANs

2026-08-27

AI Summary

本文通过构建条件Wasserstein GAN来近似复合损失模型中的后验分布，解决了在不同场景下需要重复进行贝叶斯推断的问题。

Abstract

Bayesian inference in compound loss models must often be repeated across policies, market scenarios, and prior specifications. Outside conjugate cases, this may require repeated numerical integration or Markov chain Monte Carlo (MCMC). We formulate this problem as amortized posterior approximation and construct a conditional Wasserstein generative adversarial network conditioned on sufficient statistics, prior mean and coefficient of variation, and mixture weights of prior families. Notably, a single shared generator is able to approximate the posterior laws of both the Poisson intensity and the Pareto shape parameter under mixtures of Gamma, inverse-Gaussian, and lognormal priors. We assess the approximation by simulation-based calibration and by comparisons with analytical posteriors, deterministic quadrature, and extensive MCMC simulations. In an application to data on extreme natural catastrophe losses, we produce rolling one-year posterior predictive distributions, and examine the effects of heavy-tailed severity and prior-family uncertainty on aggregate tail risk.

Problem

• Bayesian inference
• compound loss models
• posterior approximation
• numerical integration
• Markov chain Monte Carlo (MCMC)

Innovation

• conditional Wasserstein GANs
• amortized posterior approximation
• compound loss models
• Poisson intensity and Pareto shape parameter
• mixture priors