Comments (2)
We could handle the default transforms for both SVI and MCMC via an effect handler, the only difference being that for SVI it affects only the param
statements whereas for MCMC it would transform sample
statements. Another thing to note is that just transforming in the outside loop is not enough, since this also changes the PE computation.
Regarding:
initialize_model takes an additional argument named transforms.
In practice, I haven't seen models where users had to override the default transforms. I can imagine that providing a way to use custom transforms might be useful for certain models. If we provide that, it shouldn't come at the cost of users having to write boiler-plate code for models where they would just use the default transforms anyway. Let me put up something, and we can discuss the interface issues in the PR.
from numpyro.
Actually, on second thoughts, I think your approach above would be the simplest since HMC in numpyro doesn't have access to the model directly.
from numpyro.
Related Issues (20)
- Using deprecated `jax.core.safe_map` in `ops/provenance.py` HOT 7
- Normalizing Flow MCMC HOT 4
- `AutoNormal`, `AutoDelta`, and `AutoGuideList` do not support subsamples of variable size. HOT 2
- Speeding up NUTS and MCMC in tests HOT 3
- random_flax_module broken HOT 4
- Can't pickle MCMC object (MixedHMC kernel) when `chain_method="parallel"` HOT 3
- Adding HMCECS proxy functions HOT 2
- AttributeError: Can't pickle local object 'ESS.DifferentialMove.<locals>.make_differential_move.<locals>.differential_move' HOT 2
- [FR] MaskTransform HOT 2
- tracer error in blocked AutoGuide HOT 12
- Custom sampling per site for non-HMC approaches HOT 3
- `TransformedDistribution` support too broad when using `AffineTransform` transformation? HOT 2
- Correct control_flow.cond usage HOT 6
- Factor Analysis/PPCA Tutorial HOT 1
- Censoring Example HOT 1
- numpyro.deterministic static on infer.Predictive HOT 13
- [FR] Support for different supports in component distributions for mixture models HOT 5
- ImportError: cannot import name 'CAR' from "numpyro.distrubutions.continuous' HOT 2
- Use biased autocorrelation estimate by default HOT 1
- mean_accept_prob significantly different after warmup HOT 8
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from numpyro.