Metadata-Version: 2.1
Name: mclmc
Version: 0.2.5
Summary: Faster gradient based sampling
Author: Jakob Robnik
License: Apache 2.0
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.md

# MicroCanoncial Hamiltonian Monte Carlo (MCHMC)

![poster](img/github_poster.png)


You can check out the tutorials:
- [getting started](notebooks/tutorials/intro_tutorial.ipynb): sampling from a standard Gaussian (sequential sampling)
- [advance tutorial](notebooks/tutorials/advanced_tutorial.ipynb): sampling the hierarchical Stochastic Volatility model for the S&P500 returns data (sequential sampling)

Julia implementation is available [here](https://github.com/JaimeRZP/MicroCanonicalHMC.jl).

The associated papers are:
- [method and benchmark tests](https://arxiv.org/abs/2212.08549)
- [formulation as a stochastic process and first application to the lattice field theory](https://arxiv.org/abs/2303.18221)

If you have any questions do not hesitate to contact me at jakob_robnik@berkeley.edu

![ensamble](img/rosenbrock.gif)
