pocoMC is a Python implementation of the Preconditioned Monte Carlo method for accelerated Bayesian inference
Getting started
Brief introduction
pocoMC utilises a Normalising Flow in order to precondition the target distribution by removing any correlations between its parameters. The code then generates posterior samples, that can be used for parameter estimation, using a powerful adaptive Sequential Monte Carlo algorithm manifesting a sampling effiency that can be orders of magnitude higher than without precondition. Furthermore, pocoMC also provides an unbiased estimate of the model evidence that can be used for the task of Bayesian model comparison.
Documentation
Read the docs at pocomc.readthedocs.io for more information, examples and tutorials.
Installation
To install pocomc using pip run:
pip install pocomcor, to install from source:
git clone https://github.com/minaskar/pocomc.git
cd pocomc
python setup.py installBasic example
For instance, if you wanted to draw samples from a 10-dimensional Rosenbrock distribution with a uniform prior, you would do something like:
import pocomc as pc
import numpy as np
from scipy.stats import uniform
n_dim = 10 # Number of dimensions
prior = pc.Prior(n_dim*[uniform(-10.0, 20.0)]) # U(-10,10)
def log_likelihood(x):
return -np.sum(10.0*(x[:,::2]**2.0 - x[:,1::2])**2.0 \
+ (x[:,::2] - 1.0)**2.0, axis=1)
sampler = pc.Sampler(
prior=prior,
likelihood=log_likelihood,
vectorize=True,
)
sampler.run()
samples, weights, logl, logp = sampler.posterior() # Weighted posterior samples
logz, logz_err = sampler.evidence() # Bayesian model evidence estimate and uncertaintyAttribution & Citation
Please cite the following papers if you found this code useful in your research:
@article{karamanis2022accelerating,
title={Accelerating astronomical and cosmological inference with preconditioned Monte Carlo},
author={Karamanis, Minas and Beutler, Florian and Peacock, John A and Nabergoj, David and Seljak, Uro{\v{s}}},
journal={Monthly Notices of the Royal Astronomical Society},
volume={516},
number={2},
pages={1644--1653},
year={2022},
publisher={Oxford University Press}
}
@article{karamanis2022pocomc,
title={pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology},
author={Karamanis, Minas and Nabergoj, David and Beutler, Florian and Peacock, John A and Seljak, Uros},
journal={arXiv preprint arXiv:2207.05660},
year={2022}
}Licence
Copyright 2022-Now Minas Karamanis and contributors.
pocoMC is free software made available under the GPL-3.0 License. For details see the LICENSE file.
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