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

I've noticed that there are a lot more tutorials/examples around VI
but less around sampling. I thought I might write up some of the sampling
examples as notebooks and add a sampling example for bayesian nets.

Is this something that would be helpful?

I see that the Gumbel distribution has been created based on this link: https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/Gumbel
This is the xi =0 case per https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution

I would like to use a more generalized version of the extreme value distrubutions allowing xi to be non-zero; Gumbel xi =0, Frechet xi > 0, and/or W

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,

Ankit Shah and I are trying to use Gen to support a project and would love the addition of a dirichlet distribution

The hyperloglog unit tests depend on the presence of /usr/share/dict/words (see https://github.com/tylertreat/BoomFilters/blob/611b3dbe80e85df3a0a10a43997d4d5784e86245/hyperloglog_test.go#L34). It would be helpful to include the test data in the repo to ensure the tests work without modification on systems that don't follow that dictionary path convention or don't load a dictionary package.

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/

I don't know what the plans are for this software, but if there is a reasonable chance that it will require bug fixes or new features in the future, I would recommend that someone experienced with the system spend maybe 1-3 hours on basic developer documentation - just enough to orient a new person to the code.

If no more work will ever be done, this issue is moot and should be closed as a 'won

Summary:

Documentation of optimizing does not appear accurate, specifically the init parameter

Description:

The description of the init parameter of optimizing is identical to that of sampling, with reference to chains, which optimizing does not use (as far as I understand).

Behavior does not seem to correlate with choices of init either. For example, initializing

Refer to the discussion in https://forum.pyro.ai/t/hmm-like-model-with-sequences-of-different-lengths/1507.

We rely on Node's custom inspect method mechanism to pretty print distributions, but it looks like this stopped working since around Node v12. The most noticeable effect of this is that when running a program such as Infer(flip) at the command line, the output will be something like `{"probs":[

The current example on MDN from Edward tutorials needs small modifications to run on edward2. Documentation covering these modifications will be appreciated.

I've made a small change to the documentation, then tried to push them live (via the handy-dandy script) and it failed miserably. Tried to fix my installation of mkdocs, to no avail. @zaxtax can you give it a try?

• add note that carat is actually bitwise XOR so the things generated from the first box shouldn't actually be run (they later get turned into the right thing in runify

The new plotting functionality, courtesy of @nathbo, it great. It's currently undocumented other than in the readme though. Would be good if a page could be added to the docs describing the API and giving a couple of basic usage examples.

Remove examples that are not part of the book. Migrate to webppl repository, forest, or specific research repositories as appropriate.

https://github.com/probcomp/metaprob/blob/c1a01ea02c51ea0f8fedebd45954914d7645bde9/src/metaprob/distributions.clj#L55-L60

In the scorer, argument x should be checked to lie in the interval [a,b].

@lawmurray suggested adding examples of how to implement different styles of probabilistic programming languages on top of Funsor. We could implement this as a directory examples/ppl-zoo or examples/rosetta or something.

Tasks

(@eb8680 @lawmurray I've tried to populate with representative examples, but I think you both have clearer views of the landscape; feel free to update/add to/remove f