Metropolis-Hastings view on variational inference and adversarial training

ICLR 2019 Kirill NeklyudovEvgenii EgorovPavel ShvechikovDmitry Vetrov

A significant part of MCMC methods can be considered as the Metropolis-Hastings (MH) algorithm with different proposal distributions. From this point of view, the problem of constructing a sampler can be reduced to the question - how to choose a proposal for the MH algorithm?.. (read more)

PDF Abstract

Code


No code implementations yet. Submit your code now

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.