Bayesian Inference with JAX
Project description
Bayinx: Bayesian Inference with JAX
Bayinx is an embedded probabilistic programming language in Python, powered by JAX. It is heavily inspired by and aims to have feature parity with Stan, but extends the types of objects you can work with and focuses on normalizing flows variational inference for sampling.
Roadmap
- Allow shape definitions to include expressions (e.g., shape = 'n_obs + 1' will evaluate to the correct specification)
- Find a nice way to track the ELBO trajectory to implement early stoppage (tolerance currently does nothing).
- Allow users to specify custom tolerance criteria.
- Nodes carry bounds for their support (i.e., node.obj ∈ [node._lb, node._ub]) which are used to check if inputs to distributions are valid (e.g., a node inputted as the scale of a normal dist must have
node._lb >= 0)
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