Comments (1)
rlouf
commented on May 30, 2024
1
So I had a quick look. It is a new warmup scheme for the most part and here's what would be necessary:
- a jittered version of HMC, which is trivial to implement here;
- Dual averaging;
- Mass matrix adaptation;
- Their tuning algorithm for the scale of the trajectory length,
T, which requires the Adam optimizer.
(2) and (3) are part of the Stan warmup and I already have an implementation of Adam in MCX that I can just copy/paste. Will implement once we have a drop in replacement for NUTS + Stan warmup.
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Related Issues (20)
- Refactor proposal.py HOT 1
- Implement the Schrödinger-Föllmer sampler HOT 2
- Specification of sampler HOT 4
- The ESS calculation for 1 chain
- Add progress bar to `run_inference_loop` HOT 2
- Remove `transform` from MCLMC, and place it in `run_inference_loop`
- Add inverse_mass_matrix to MCLMC HOT 3
- Merge dynamic_hmc and hmc HOT 3
- Improve SamplingAlgorithm design for init_fn and step_fn HOT 1
- Separate out Halton proposal length from CHEES HOT 1
- MCLMC Info should not scale kinetic_change again
- 👋 Blackjax Meeting -
- Integration with Inference Gym HOT 6
- 👋 Blackjax Meeting -
- 👋 Blackjax Meeting -
- BlackJAX Paper HOT 3
- CHMC from A Family of MCMC Methods on Implicitly Defined Manifolds
- 👋 Blackjax Meeting -
- Removing high-level API classes HOT 4
- 👋 Blackjax Meeting -
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