Brandon T. Willard

Results 358 comments of Brandon T. Willard

Ah, yeah, I left off with the idea of incrementally pickling the session in [`_[save|load]_chunk_state`](https://github.com/mpastell/Pweave/compare/master...brandonwillard:caching-changes#diff-2747ccbd23b5ea3c1c42eb01071e5a6eR166). This idea isn't all that efficient/feasible without, perhaps, an [incremental approach](https://github.com/mpastell/Pweave/compare/master...brandonwillard:caching-changes#diff-2747ccbd23b5ea3c1c42eb01071e5a6eR221). At around the same...

Yeah, I think that any non-naive caching (e.g. more than just caching output and validating against source text differences) is necessarily language-specific. However, it seems like more than a few...

This needs to be rebased.

Looks like the tests are failing due to an old version of `actions/setup-python`. I'll put in a PR to fix that; you'll need to rebase after that's merged.

All right, if you rebase your two PRs, the tests should run.

If I recall correctly, TFP uses effectively the same shape/dimension design outlined [here](https://gist.github.com/brandonwillard/d4ec9f9f2753d1d9ef1aab71799860f7) and later implemented in the Theano `RandomVariable`, but under a different naming scheme (e.g. "batches" or "event"...

Yes, see [`kubectl port-forward`](https://kubernetes.io/docs/reference/generated/kubectl/kubectl-commands#port-forward).

[Here's a prototype conversion function](https://gist.github.com/brandonwillard/4fdeedd3d6eafb2b977a453149b6c9d1) that does almost everything mentioned above. There is a lot of room for error, especially when a `Distribution` demands test values or uses instance fields...

I have a manually constructed Theano FFBS sampler for DLMs in [this article](https://brandonwillard.github.io/dynamic-linear-models-in-theano.html), along with a non-trivial scale-mixture extension to non-Gaussian observations. This is **exactly** the kind of thing we...

We can start by creating a goal, `normalizeo`, and make it able to normalize according to all the standard scalar group axioms under a simple ordering like lexicographical path ordering....