Pre-warmup automatic reparameterization?

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Mike Lawrence

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Apr 7, 2022, 3:53:00 PMApr 7
to TensorFlow Probability
Has the approach of Gorinova et al (2019) made it into TFP in any form? I'd love to cross centered/non-centered tuning off my list of model-building decisions!

Mike Lawrence

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Apr 7, 2022, 4:20:54 PMApr 7
to TensorFlow Probability
Oh, I see that at least the numpyro folks implemented it...

Christopher Suter

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Apr 8, 2022, 12:56:11 PMApr 8
to Mike Lawrence, TensorFlow Probability
Hi Mike, we don't have anything pre-packaged (that I'm aware of) for the parametric semi-decentering, but here's a colab demo of non-centering Neal's funnel using `tfp.experimental.bijectors.make_distribution_bijector` (generously bestowed upon us by Dave Moore, co-author w/ Maria Gorinova on the paper you cited):


There are more examples in the docstring for make_distribution_bijector. It can do some pretty fancy stuff.

On Thu, Apr 7, 2022 at 4:20 PM Mike Lawrence <mike....@gmail.com> wrote:
Oh, I see that at least the numpyro folks implemented it...

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Pavel Sountsov

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Apr 8, 2022, 2:13:38 PMApr 8
to Christopher Suter, Mike Lawrence, TensorFlow Probability
The way you parameterize (I think) it is you first train a structured surrogate posterior via VI (see an example in the build_asvi_surrogate_posterior docstring) and then use `tfp.experimental.bijectors.make_distribution_bijector` on the learned surrogate.

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