G. Tzougas

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

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 condi...