Updated LocScaleReparam's documentation

[Madhav-Kanda]

Refer #1598

[fehiepsi]

Could you change this to something like:

    :param float centered: optional centered parameter. If None (default) learn
        a per-site per-element centering parameter in ``[0,1]`` initialized at value 0.5.
        To sample the parameter, consider using :class:`~numpyro.handlers.lift` handler with a
        prior like ``Uniform(0, 1)`` to cast the parameter to a latent variable. If 0, fully

[Madhav-Kanda]

Hi, I would change this, but based on my understanding of the current implementation, it would not learn the center param even when we keep it None. It would simply keep the centered param as 0.5. To learn the correct parameter based on Maria Gorinova's paper, we will have to use Variational Inference. Please correct me if I need to correct something. Thanks for the quick response.

[fehiepsi]

That's why we say this is a parameter, not a latent variable. When using MCMC, you are drawing samples of latent variables. When you say "optimizing", it typically means that you are using inference methods with optimizers like SGD, Adam and gradient descent to optimize the corresponding parameters. When you say "sampling", it means that you are using MCMC to draw samples of latent "random variable".

[fehiepsi]

note that in numpyro, MCMC does not sample "parameters", it only samples latent variables. If you think the word "parameters" is not clear enough, I think you can change it to something like

If None (default) create a ``numpyro.param`` site for a per-site per-element centering parameter ...

[Madhav-Kanda]

Oh, okay. Thank you!

[fehiepsi]

Thanks @Madhav-Kanda!