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

We would like all GPflow kernels to broadcast across leading dimensions. For most of them, this is implemented already (#1308); this issue is to keep track of the ones that currently don't:

• ArcCosine
• Coregion
• Periodic
• ChangePoints
• Convolutional
• all MultioutputKernel subclasses

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

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.

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?

@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

There was recently some discussion on Stan Discourse that resulted in this PR in Stan and this PR in PyMC3. They make some changes to the NUTS criterion to handle a previously undiscovered case where the N

I was browsing through the docs and noticed that if I click the "source" button associated with a docstring, I am taken to julia base instead of to the definition of the function in TransformVariables.jl

In https://github.com/bat/BAT.jl/blob/master/src/integration/ahmi/harmonic_mean_integration.jl#L453 integral estimates that are nan or inf (presumably when the hyper-rectangles are empty) are replaced by the estimate from the full sample. This should not be the case