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https://github.com/jax-ml/bayeux
State of the art inference for your bayesian models.
https://github.com/jax-ml/bayeux
Last synced: 11 days ago
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State of the art inference for your bayesian models.
- Host: GitHub
- URL: https://github.com/jax-ml/bayeux
- Owner: jax-ml
- License: apache-2.0
- Created: 2023-11-02T16:52:57.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-01T14:15:40.000Z (about 1 month ago)
- Last Synced: 2024-10-29T10:08:24.099Z (12 days ago)
- Language: Python
- Homepage: https://jax-ml.github.io/bayeux/
- Size: 1.37 MB
- Stars: 157
- Watchers: 5
- Forks: 6
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# Bayeux
*Stitching together models and samplers*
[![Unittests](https://github.com/jax-ml/bayeux/actions/workflows/pytest_and_autopublish.yml/badge.svg)](https://github.com/jax-ml/bayeux/actions/workflows/pytest_and_autopublish.yml)
[![PyPI version](https://badge.fury.io/py/bayeux_ml.svg)](https://badge.fury.io/py/bayeux_ml)`bayeux` lets you write a probabilistic model in JAX and immediately have access to state-of-the-art inference methods. The API aims to be **simple**, **self descriptive**, and **helpful**. Simply provide a log density function (which doesn't even have to be normalized), along with a single point (specified as a [pytree](https://jax.readthedocs.io/en/latest/pytrees.html)) where that log density is finite. Then let `bayeux` do the rest!
## Installation
```bash
pip install bayeux-ml
```
## QuickstartWe define a model by providing a log density in JAX. This could be defined using a probabilistic programming language (PPL) like [numpyro](examples/numpyro_and_bayeux), [PyMC](examples/pymc_and_bayeux), [TFP](examples/tfp_and_bayeux), distrax, oryx, coix, or directly in JAX.
```python
import bayeux as bx
import jaxnormal_density = bx.Model(
log_density=lambda x: -x*x,
test_point=1.)seed = jax.random.key(0)
opt_results = normal_density.optimize.optax_adam(seed=seed)
# OR!
idata = normal_density.mcmc.numpyro_nuts(seed=seed)
# OR!
surrogate_posterior, loss = normal_density.vi.tfp_factored_surrogate_posterior(seed=seed)
```## Read more
* [Defining models](inference)
* [Inspecting models](inspecting)
* [Testing and debugging](debug_mode)
* Also see `bayeux` integration with [numpyro](examples/numpyro_and_bayeux), [PyMC](examples/pymc_and_bayeux), and [TFP](examples/tfp_and_bayeux)!*This is not an officially supported Google product.*