This is an awesome library, thanks @ddbourgin!!

Users might not know the best way to install this package and try it out. (I didn't, so I eventually just copied the source files.)
Neither the readme nor readthedocs have install instructions.

I couldn't find it on PyPi or Anaconda, and there doesn't appear to be a pyproject.toml, setup.cfg, setup.py, or conda recipe.

Moreover, the t

this doesn't seem very well documented at present.

Description of your problem

Interpolated Docs are missing sample plot. One should be added
https://docs.pymc.io/api/distributions/continuous.html#pymc3.distributions.continuous.Interpolated

Please provide any additional information below.
See example from Normal plot for

Summary:

Regarding the parameters of the dual averaging optimization for the stepsize in the warmup, all parameters can be set by the user except mu. For certain models, the initial choice of mu can result in a drastic drop of the stepsize for subsequent iterations. This might lead to extra computation time. As mu is adjusted for each window of the mass matrix adjustment, this can in e

There seems to be some little bugs in these examples. For example
for t in range(iters): labeled_indices = (np.random.randint(0, n_labeled, size=batch_size)) x_labeled_batch = x_labeled[labeled_indices]

It throws the error that
TypeError: Only integers, slices (:), ellipsis (...), tf.newaxis (None`) and scalar tf.int32/tf.int64 tensors are valid indices, got array([17,

The following pkgdown R library allows generating a doc site based on GitHub repo information (e.g. the README.md, developers etc). Wondering whether we can do something similar with Turing sub-modules, particularly build a page from the README.md file and release notes.

https://pkgdown.r-lib.org/

This should reduce the number of duplicated documentation snippets and replace @inheritParams tags.

Summary:

Right now there is a wiki page:

https://github.com/stan-dev/rstan/wiki/RStan-Mojave-Mac-OS-X-Prerequisite-Installation-Instructions

about a particular aspect of Mac OS X installation. Can we roll that into the basic install instructions?

https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started

Otherwise, I fear people won't find it. Right now, there's a bunch of

Description

Following the tutorial raises the following:

bn = bn.fit_cpds(train, method="BayesianEstimator", bayes_prior="K2")

Would be great to have a fully working jupyter notebook as an example.

Steps to Reproduce

/usr/local/lib/python3.7/site-packages/causalnex/network/network.py in fit_cpds(self, data, method, bayes_prior, equivalent_sample_size)
    344 
    345  

Hi @JavierAntoran @stratisMarkou,

First of all, thanks for making all of this code available - it's been great to look through!

Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac

Now that we have an IAF autoguide, it should be relatively straightforward to add an IAF layer to our VAE example and do some comparisons.

the links in the README pull up HTML pages that seem to be missing images for the models.

Perhaps I could convert them to markdown/latex cells so they'll guarantee rendering?

Love this repo, by the way. Writing a software library (and demos for it) based on my thesis work in inverse problems, and this helps motivate what "good tutorials" can look like.

@bgoodri I just noticed that none of the CRAN versions of the vignettes have any output from the code chunk no output anymore (e.g. if you look at https://cran.r-project.org/web/packages/rstanarm/vignettes/count.html
or any of the other vignettes). That is, it looks like none of the code is getting evaluated. I know that we're using that mechanism with params$EVAL to avoid evaluating the vignet