Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

AMAGOLD support #283

Open
Vaibhavdixit02 opened this issue May 19, 2021 · 1 comment
Open

AMAGOLD support #283

Vaibhavdixit02 opened this issue May 19, 2021 · 1 comment

Comments

@Vaibhavdixit02
Copy link
Contributor

Came across http://proceedings.mlr.press/v108/zhang20e.html which seems to guarantee asymptotic convergence with the goodness of efficiency with SGHMC. Adding here to see if there is interest from users or plans from the devs to add this.

@Red-Portal
Copy link
Member

Stochastic gradient MCMC methods, in general, are very young, and radically new approaches emerge every year. Unless AMAGOLD specifically proves to be real-world ready, I doubt that it would earn a high priority for development. Has any other competing platform like Stan or PyMC implemented AMAGOLD or any stochastic gradient MCMC method really?

@yebai yebai transferred this issue from TuringLang/Turing.jl Dec 16, 2021
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants