Approximate Inference with Amortised MCMC

  title={Approximate Inference with Amortised MCMC},
  author={Yingzhen Li and Richard E. Turner and Qiang Liu},
We propose a novel approximate inference algorithm that approximates a target distribution by amortising the dynamics of a user-selected MCMC sampler. The idea is to initialise MCMC using samples from an approximation network, apply the MCMC operator to improve these samples, and finally use the samples to update the approximation network thereby improving its quality. This provides a new generic framework for approximate inference, allowing us to deploy highly complex, or implicitly defined… CONTINUE READING
Recent Discussions
This paper has been referenced on Twitter 18 times over the past 90 days. VIEW TWEETS
11 Citations
60 References
Similar Papers

Similar Papers

Loading similar papers…