Fix README links by separating README from docs index.

README.md

@@ -35,10 +35,10 @@ surrogate_posterior, loss = normal_density.vi.tfp_factored_surrogate_posterior(s
 
 ## 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)!
+* [Defining models](docs/inference.md)
+* [Inspecting models](docs/inspecting.md)
+* [Testing and debugging](docs/debug_mode.md)
+* Also see `bayeux` integration with [numpyro](docs/examples/numpyro_and_bayeux.ipynb), [PyMC](docs/examples/pymc_and_bayeux.ipynb), and [TFP](docs/examples/tfp_and_bayeux.ipynb)!
 
 
 *This is not an officially supported Google product.*

docs/index.md

@@ -1 +1,44 @@
---8<-- "README.md"
+# 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
+```
+## Quickstart
+
+We 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 jax
+
+normal_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.*