BayesPy provides tools for Bayesian inference with Python. The user constructs a model as a Bayesian network, observes data and runs posterior inference.

Introduction · Project information · Similar projects · Contributors · Version history · User guide · Installation · Quick start guide · Constructing the ...

28 янв. 2023 г. · Some popular libraries include PyMC3, NiLearn, pgmpy, PyBN, and Orange3-Bayesian-Networks.

PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) ... PyMC · RELEASE-NOTES.md · Issues 255 · Pull requests 69

PyMC is a probabilistic programming library for Python that allows users to build Bayesian models with a simple Python API and fit them using Markov chain ... Documentation · API · History · Installation

pgmpy is a Python package for working with Bayesian Networks and related models such as Directed Acyclic Graphs, Dynamic Bayesian Networks, and Structural ... Issues 258 · CHANGELOG.md · Discussions · Pull requests 40

Since we are learning in this chapter we will code one, but for the rest of the book we are going to use engines available in Python libraries. There are many ...

20 июл. 2020 г. · Project Description. Probabilistic reasoning module on Bayesian Networks where the dependencies between variables are represented as links among ...

PyMC is likely the most popular package for probabilistic programming in Python, and its computations are built on what was originally a deep learning library.

ArviZ is a Python package for exploratory analysis of Bayesian models. It serves as a backend-agnostic tool for diagnosing and visualizing Bayesian inference. Python · Getting Started · Example gallery · 0.14.0