Bayesian Libraries

13 Components & Libraries

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Bayesian Libraries

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of rea…

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

To install the current release: Participating discussions via the following IM groups is also welcomed. Over the past few years, NNI has received thousands of feedbacks on GitHub issues, and pull req…

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTensor

PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensi…

Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop

This project contains Jupyter notebooks of many the algorithms presented in Christopher Bishop's Pattern Recognition and Machine Learning book, as well as replicas for many of the graphs presented in…

GemPy is an open-source, Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation data. It also offers support for stochastic modeling to address parameter and model uncertainties.

Welcome to the era of GemPy v3! We are thrilled to announce the release of the latest version, a product of meticulous planning, redesign, and rigorous testing. While the core essence remains intact,…

GemPy is an open-source, Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation data. It also offers support for stochastic modeling to address parameter and model uncertainties.

Open-source, implicit 3D structural geological modeling in Python. We provide the latest release version of GemPy via PyPi package services. We highly recommend using PyPi, The following versions are…

M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4.

This is the Jupyter notebook version of the following book: If you use either book or code in a scholarly publication, please cite as: M. Beyeler, (2017). Machine Learning for OpenCV. Packt Publishin…

General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python

Jupyter Notebooks on the topics of statistical analysis, mathematics, and numerical/sceintific computing (in Python).

Python package for Bayesian Machine Learning with scikit-learn API

There are several ways to contribute (and all are welcomed)

ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.

ParaMonte has been developed while bearing the following design goals in mind: The ParaMonte library relies on three independent parallelism paradigms for parallel applications: The ParaMonte librari…

Example PyMC3 project for performing Bayesian data analysis using a probabilistic programming approach to machine learning.

This notebook contains the code required to conduct a Bayesian data analysis on data collected from a set of multiple-lot online auction events executed in Europen markets, over the course of a year.…

Modular and scalable computational imaging in Python with GPU/out-of-core computing.

In the realm of computer vision 📷, digital image restoration and enhancement techniques have established themselves as indispensable pillars. These techniques, aiming to restore and elevate the qual…

A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

This is an implementation of the following paper: If you find this code useful, please consider citing: Given the exact differentiable characterization of a DAG, we are interested in solving the foll…

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