pocomc 0.0.12

Preconditioned Monte Carlo

pocoMC is a Python implementation of the Preconditioned Monte Carlo method for accelerated Bayesian inference

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

ndim = 10  # Number of dimensions

def log_prior(x):
    if np.any((x < -10.0) | (x > 10.0)):  # If any dimension is out of bounds, the log prior is -infinity
        return -np.inf 
    else:
        return -const

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)


nwalkers = 1000
prior_samples = np.random.uniform(size=(nwalkers, ndim), low=-10.0, high=10.0)

sampler = pc.Sampler(nwalkers,
                     ndim,
                     log_likelihood,
                     log_prior,
                     vectorize_likelihood=True,
                     bounds=(-10.0, 10.0)
                    )
sampler.run(prior_samples)

results = sampler.results # Dictionary with results

Documentation

Read the docs at pocomc.readthedocs.io

Installation

To install pocomc using pip run:

pip install pocomc

Attribution

Please cite the following papers if you found this code useful in your research:

@article{karamanis2022pocomc,
  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={in prep},
  year={2022}
}

Licence

Copyright 2022-Now Minas Karamanis and contributors.

zeus is free software made available under the GPL-3.0 License. For details see the LICENSE file.