Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications

@article{Mcke2021MarkovCG,
  title={Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications},
  author={Nikolaj Takata M{\"u}cke and Benjamin Sanderse and Sander M. Boht'e and Cornelis W. Oosterlee},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.12408}
}
In the context of solving inverse problems for physics applications within a Bayesian framework, we present a new approach, Markov Chain Generative Adversarial Neural Networks (MCGANs), to alleviate the computational costs associated with solving the Bayesian inference problem. GANs pose a very suitable framework to aid in the solution of Bayesian inference problems, as they are designed to generate samples from complicated high-dimensional distributions. By training a GAN to sample from a low… 
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