Library for jax based affine-invariant MCMC sampling
Project description
jaims
A jax based affine-invariant MCMC sampler that can leverage GPUs to speed up sampling for computationally intensive likelihoods. It implements the Goodman-Weare algorithm as described in dfm++ and is inspired by the popular 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 jaims
, please clone this repository and then run python setup.py install
inside it
You can also install this via pip
using
pip install jaims
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
The API for jaims
is slightly different from emcee
. This might change in the future.
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