Likelihood-free inference toolbox. Supported features include:

• Composition and fitting of distributions;
• Likelihood-free inference from classifiers;
• Parameterized supervised learning;
• Calibration tools.

Note: carl is still in its early stage of development. Join us if you feel like contributing!

The following dependencies are required:

• Numpy >= 1.10
• Scipy >= 0.17
• Scikit-Learn >= 0.18-dev
• Theano >= 0.8-dev

Once satisfied, carl can be installed from source using the following commands:

git clone https://github.com/diana-hep/carl.git
cd carl
python setup.py install

Illustrative examples serving as documentation can be found under the examples/ directory.

Extended details regarding likelihood-free inference with calibrated classifiers can be found in the companion paper:

"Approximating Likelihood Ratios with Calibrated Discriminative Classifiers", Kyle Cranmer, Juan Pavez, Gilles Louppe.
http://arxiv.org/abs/1506.02169

@misc{carl,
  author       = {Gilles Louppe and Kyle Cranmer and Juan Pavez},
  title        = {carl: a likelihood-free inference toolbox},
  month        = mar,
  year         = 2016,
  doi          = {10.5281/zenodo.47798},
  url          = {http://dx.doi.org/10.5281/zenodo.47798}
}