MH Tessler
MH Tessler
To add to this. In RWebPPL, the user can either pass in the inference algorithm options object from R, or just write it in the WebPPL program as we would...
Just wanted to bump this as an issue. Here is my minimal example: ``` var myDist = Infer({model: function(){ return {a: flip(), b: flip()} }}) myDist.score({b: true, a: true}) ```
My feeling is that people who do BDA will want to see these, and so it would be good to have these sooner rather than later. I'm pretty sure (at...
I have not used `groupBy` before
FWIW, I wrote some (very simple) webppl tutorials this summer for various demos: [basic programming with webppl](https://github.com/mhtess/webppl-tutorials/blob/master/tutorials/intro.md) and [introduction to bayes rule with rejection](https://github.com/mhtess/webppl-tutorials/blob/master/tutorials/bayes-rule.md)
Bumping this. Anything we can do to help diagnose the issue?
We need to test for each of the `webppl()` arguments. Each of the inference algorithms (`rejection`, `mcmc`, `enumerate`, `incrementalMH`,...) should probably be tested with the `inference_opts` argument. Here is a...
Some ideas (not sure if they're necessary and/or feasible) - [ ] other functions that ship with rwebppl (`get_samples`, `install_webppl()`, `install_webppl_package()`, `link_webppl()` if that will still exist) - [ ]...
Add test for precision in data passing, a la #57
updated list of tests to make - [ ] test for `kill_webppl()` (#22) - [ ] test for precision in data passing (#57) - [ ] multiple chains - [...