Welcome to probabilistic_models’s documentation!

Welcome to probabilistic_models’s documentation!#

The probabilistic_models package contains fast and flexible implementations for various probabilistic models. This package provides a clean, unifying and well documented API to probabilistic models. Just like sklearn does for classical machine learning models.

Install the package via pip:

pip install probabilistic_model

Supported Models#

  • Continuous Distributions

    • Gaussian Distribution

    • Uniform Distribution

  • Discrete Distributions

    • Categorical Distribution

    • Integer Distribution

  • Bayesian Networks

  • Probabilistic Circuits / Sum Product Networks

    • Random and Tensorized SPNs

    • Nyga Distributions

    • Joint Probability Trees

    • Conditional SPNs

Supported Inferences#

  • Likelihoods

  • Sampling

  • Marginal Probabilities

  • Marginal Distributions

  • Conditional Distributions

  • Modes

  • Moments

  • \(L_1\) distances

Citing probabilistic_model#

If you use this software for publications, please cite it as below.

@software{schierenbeck2024pm,
author = {Schierenbeck, Tom},
title = {probabilistic_model: A Python package for probabilistic models},
url = {https://github.com/tomsch420/probabilistic_model},
version = {7.1.0},
}
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