Library for jax based affine-invariant MCMC sampling
Project description
# jammer
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A jax based affine-invariant MCMC hammer that can leverage GPUs to speed up sampling for computationally intensive likelihoods. It implements the [Goodman-Weare](https://msp.org/camcos/2010/5-1/p04.xhtml) algorithm as described in [dfm++](https://arxiv.org/abs/1202.3665) and is inspired by the popular [emcee](https://github.com/dfm/emcee) library. The just-in-time compilation together with vectorized likelihood evaluation for the walkers gives significant speed-up even on CPUs when compared to emcee
## Installation
To install jammer, please clone this repository and then run python setup.py install inside it You can also install this via pip using ` pip install jammer ` To run it on a GPU, you must have an installation of jaxlib compatible with your CUDA version. For more information, please refer to the official [guidelines](https://github.com/google/jax#installation)
The API for jammer is slightly different from emcee. This might change in the future.
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