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+
+
+# Aehmc
+
+[![Pypi][pypi-badge]][pypi]
+[![Gitter][gitter-badge]][gitter]
+[![Discord][discord-badge]][discord]
+[![Twitter][twitter-badge]][twitter]
+
+AeHMC provides implementations for the HMC and NUTS samplers in [Aesara](https://github.com/aesara-devs/aesara).
+
+[Features](#features) •
+[Get Started](#get-started) •
+[Install](#install) •
+[Get help](#get-help) •
+[Contribute](#contribute)
+
+
+
+## Get started
+
+``` python
+import aesara
+from aesara import tensor as at
+from aesara.tensor.random.utils import RandomStream
+
+from aeppl import joint_logprob
+
+from aehmc import nuts
+
+# A simple normal distribution
+Y_rv = at.random.normal(0, 1)
+
+
+def logprob_fn(y):
+ return joint_logprob(realized={Y_rv: y})[0]
+
+
+# Build the transition kernel
+srng = RandomStream(seed=0)
+kernel = nuts.new_kernel(srng, logprob_fn)
+
+# Compile a function that updates the chain
+y_vv = Y_rv.clone()
+initial_state = nuts.new_state(y_vv, logprob_fn)
+
+step_size = at.as_tensor(1e-2)
+inverse_mass_matrix=at.as_tensor(1.0)
+(
+ next_state,
+ potential_energy,
+ potential_energy_grad,
+ acceptance_prob,
+ num_doublings,
+ is_turning,
+ is_diverging,
+), updates = kernel(*initial_state, step_size, inverse_mass_matrix)
+
+next_step_fn = aesara.function([y_vv], next_state, updates=updates)
+
+print(next_step_fn(0))
+# 0.14344008534533775
+```
+
+## Install
+
+The latest release of AeHMC can be installed from PyPI using ``pip``:
+
+``` bash
+pip install aehmc
+```
+
+Or via conda-forge:
+
+``` bash
+conda install -c conda-forge aehmc
+```
+
+The current development branch of AeHMC can be installed from GitHub using ``pip``:
+
+``` bash
+pip install git+https://github.com/aesara-devs/aehmc
+```
+
+## Get help
+
+Report bugs by opening an [issue][issues]. If you have a question regarding the usage of AeHMC, start a [discussion][discussions]. For real-time feedback or more general chat about AeHMC use our [Discord server][discord] or [Gitter room][gitter].
+
+## Contribute
+
+AeHMC welcomes contributions. A good place to start contributing is by looking at the [issues][issues].
+
+If you want to implement a new feature, open a [discussion][discussions] or come chat with us on [Discord][discord] or [Gitter][gitter].
+
+[contributors]: https://github.com/aesara-devs/aehmc/graphs/contributors
+[contributors-badge]: https://img.shields.io/github/contributors/aesara-devs/aehmc?style=flat-square&logo=github&logoColor=white&color=ECEFF4
+[discussions]: https://github.com/aesara-devs/aehmc/discussions
+[downloads-badge]: https://img.shields.io/pypi/dm/aehmc?style=flat-square&logo=pypi&logoColor=white&color=8FBCBB
+[discord]: https://discord.gg/h3sjmPYuGJ
+[discord-badge]: https://img.shields.io/discord/1072170173785723041?color=81A1C1&logo=discord&logoColor=white&style=flat-square
+[gitter]: https://gitter.im/aesara-devs/aehmc
+[gitter-badge]: https://img.shields.io/gitter/room/aesara-devs/aehmc?color=81A1C1&logo=matrix&logoColor=white&style=flat-square
+[issues]: https://github.com/aesara-devs/aehmc/issues
+[releases]: https://github.com/aesara-devs/aehmc/releases
+[twitter]: https://twitter.com/AesaraDevs
+[twitter-badge]: https://img.shields.io/twitter/follow/AesaraDevs?style=social
+[pypi]: https://pypi.org/project/aehmc/
+[pypi-badge]: https://img.shields.io/pypi/v/aehmc?color=ECEFF4&logo=python&logoColor=white&style=flat-square
diff --git a/README.rst b/README.rst
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-|Tests Status| |Coverage|
-
-AeHMC provides MCMC sampling algorithms written in `Aesara `_.
-
-Features
-========
-- Sample from an (unnormalized) probability distribution using Hamiltonian Monte
- Carlo and the No U-Turn Sampler.
-
-Example
-=======
-
-.. code-block:: python
-
- import aesara
- from aesara import tensor as at
- from aesara.tensor.random.utils import RandomStream
-
- from aeppl import joint_logprob
-
- from aehmc import nuts
-
- # A simple normal distribution
- Y_rv = at.random.normal(0, 1)
-
-
- def logprob_fn(y):
- return joint_logprob(realized={Y_rv: y})[0]
-
-
- # Build the transition kernel
- srng = RandomStream(seed=0)
- kernel = nuts.new_kernel(srng, logprob_fn)
-
- # Compile a function that updates the chain
- y_vv = Y_rv.clone()
- initial_state = nuts.new_state(y_vv, logprob_fn)
-
- step_size = at.as_tensor(1e-2)
- inverse_mass_matrix=at.as_tensor(1.0)
- (
- next_state,
- potential_energy,
- potential_energy_grad,
- acceptance_prob,
- num_doublings,
- is_turning,
- is_diverging,
- ), updates = kernel(*initial_state, step_size, inverse_mass_matrix)
-
- next_step_fn = aesara.function([y_vv], next_state, updates=updates)
-
- print(next_step_fn(0))
- # 0.14344008534533775
-
-
-Installation
-============
-
-The latest release of AeHMC can be installed from PyPI using ``pip``:
-
-::
-
- pip install aehmc
-
-Or via conda-forge:
-
-::
-
- conda install -c conda-forge aehmc
-
-
-The current development branch of AeHMC can be installed from GitHub using ``pip``:
-
-::
-
- pip install git+https://github.com/aesara-devs/aehmc
-
-
-
-.. |Tests Status| image:: https://github.com/aesara-devs/aehmc/actions/workflows/test.yml/badge.svg?branch=main
- :target: https://github.com/aesara-devs/aehmc/actions/workflows/test.yml
-.. |Coverage| image:: https://codecov.io/gh/aesara-devs/aehmc/branch/main/graph/badge.svg?token=L2i59LsFc0
- :target: https://codecov.io/gh/aesara-devs/aehmc