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

General information:

• emcee version: 3.0.2
• platform: MacOS X
• installation method (pip/conda/source/other?): pip

Problem description:
The docstring for the integrated_time function still mentions the argument 'axis', which does not seem to be implemented anymore. It would be great if the axis argument could be specified, to avoid having to swap axes before passing to this func

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/

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

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

Just noticed that this isn’t discussed anywhere, but the PPC functions have always also been very useful for prior predictive checking, not just posterior checking.

@alexeid @rbouckaert @tgvaughan
Alexei and I are proposing a knowledge-based package manager, and I think you should join us. The current package manager is still not informative, which does not provide enough information about features and models. We already have the features table in beast2.org, but they are not synchronised each other.

The idea is to eventually build up a system that re

When setting up tip date sampling in BEAUti, half a year is being added implicitly to the sampling date. However, this change is not reflected in BEAUti, nor in the XML that is being generated. It would be better to modify the sampling date (both in BEAUti and the XML) so that there can be no confusion.

Steps to reproduce:

1. Load data set into BEAUti
2. Guess dates
3. Select one or more seq

From Lu Cheng:

"It was said in GPStuff manual page 42 that periodic kernel was coming
from this paper
http://jmlr.org/proceedings/papers/v33/solin14.pdf

In page 907, equation (23) and GPStuff appendix, there is the canonical
periodic covariance function. And it is not obvious to find the explicit
form of quasi-periodic covariance function in section 3.5.

In the demo_periodic.m, there is alway t

Currently the User Manual section on user-defined samplers does not explain mvSaved very clearly. In addition, for learning how nimCopy works, the User Manual and roxygen entries each point to the other, so it could help to put more explanation in one of them.

For now, the serialization system is based on calling directly __getstate__ and __setstate__. We should add to_dict and from_dict methods.

Note that many objects have __get/setstate__ methods that accept arguments that can be used to improve performance (e.g. SamplerState.__getstate__(ingore_velocities=False)) and should have an equivalent argument in to/from_dict.

See also #40

The documentation for AHMC is currently sparse. Maybe we can improve the doc after AHMC API gets a bit more stable. For a good reference, see e.g.
https://docs.pymc.io/api/inference.html#pymc3.step_methods.hmc.nuts.NUTS

The current unit test for the diagnostics functions only test if the function call works. In the future, we should add proper unit tests to ensure the resulting computations are correct.

• add unit test for discretediag
• add unit test for gelmandiag
• add unit test for gewekediag
• add unit test for heideldiag
• add unit test for rafterydiag