Results 15 issues of Rob Zinkov

I've noticed that the bundled plugins are severely bit-rotted. The code for dynamically loading plugins is very brittle and does not play well with the sandboxed method Haskell libraries are...

This tries to address #4807 Right now, it passes all unit tests, but doesn't use the fast sampling procedure of `sample_posterior_predictive` The code is also not feature-complete.

enhancements

Related to #5646, the current `test_step_continuous` in `test_step.py` is a bit flaky and I'm not sure what it's designed to test. Can I get some insight into test so I...

tests
help wanted
question

This is being split off from @wrengr comment in #27 - [ ] When encountering `case b of { True -> e1; False -> e2 }` instead print `if_ b...

enhancement

This adds the `add_coords` function so that coords can be updated without needing to explicitly mentioning a model **Checklist** + [x] Make sure that [the pre-commit linting/style checks pass](https://docs.pymc.io/en/latest/contributing/python_style.html). +...

This PR introduces a way to use distributions without needing to provide a name. This means: ````python import pymc as pm with pm.Model(): x = pm.Normal(0., 1.) ```` is equivalent...

request discussion
major

### Motivation This removes arviz as an explicit dependency of Bean Machine. This will also prevent a circular dependency with arviz. ### Changes proposed Updates setup.py and moves import into...

CLA Signed

### Motivation This uses scikit-learn to show what happens when a regular regression is used and motivates the need for robustness in some examples with a plot. ### Changes proposed...

CLA Signed

### Motivation This is the PR where all implementations of MixedHMC live along with surrounding docs and tutorial. This should not be merged yet. ### Types of changes - [...

CLA Signed

### Issue Description In Bean Machine right now there is some code for converting MonteCarloSamples objects into arviz-compatible InferenceData objects. It would be better if that functionality lived in arviz.

enhancement