Recently, various deep learning approaches have yielded highly promising results  in demographic inference. One of these — Generative Adversarial Networks (GANs) — is particularly useful for mimicking real data while  exploring the joint probability distribution of parameters that produce such data. In a GAN, two neural networks – known as the Discriminator and the Generator – compete in a  zero-sum game, to both mimic the input data and learn about the process that generated it. However, standard simulation engines cannot be readily incorporated as GAN generators. Here we develop a GAN that is coupled with a Markov Chain Monte Carlo  (MCMC) algorithm, and thus allows for inference from the full posterior probability distribution of the parameters.


Bioinfo Club Abril 2022: Demographic inference using a MCMC-coupled Generative Adversarial Network

Este sitio web utiliza cookies para que usted tenga la mejor experiencia de usuario. Si continúa navegando está dando su consentimiento para la aceptación de las mencionadas cookies y la aceptación de nuestra política de cookies, pinche el enlace para mayor información.

Aviso de cookies