beanmachine
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facebookresearch
A library that allows for inference on probabilistic models
Summary: ### Motivation VI currently uses legacy world. This updates it to use the new `World` class. ### Changes proposed VI maintains its own set of `params` so that a...
Summary: I'm continuing the work I began in the previous diff. We have numerous difficulties in the "problem fixing" phase where the graph is gradually rewritten from its accumulated form...
Summary: These three performance tests do only one thing: run inference with and without a certain optimization, and measure the change in the number of nodes and edges in the...
Summary: See https://github.com/facebookresearch/beanmachine/issues/1312 This user-supplied repro with a simple model produces an internal compiler error. What's happening here is: the graph is mutable, and large graphs may be expensive to...
Summary: This diff continues the work begun in the previous; I am adding location information to error messages when known. In this diff, violations of BMG type system rules are...
Summary: The compiler error messages were a little bit awkward: `The model uses a & operation unsupported by Bean Machine Graph.` Not wrong but hard to read. We always said...
Summary: I'm continuing my efforts to: * Refactor and simplify the operator handling code to unify it with the special-function-handling code. * Ensure that all the ways to represent an...
Resolves #1817 ### Motivation Allow a user to select which point statistic they want displayed in the Marginal 1D tool: mean or median. ### Changes proposed - Added method to...
### Motivation From an internal discussion, this is a work-in-progress that shows all diagnostic tools in a single dashboard. ### Changes proposed - Renames the `.prettierrc` file to `.prettierrc.json` purely...
### Motivation As a first step of the Pytest suite refactoring and extension, the model/observation/inference choices in the existing tests are explicitly parametrised as fixtures, and local (intra-module) redundancies are...
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A library that allows for inference on probabilistic models