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Python Toolkit for Causal and Probabilistic Reasoning
Fast, flexible and easy to use probabilistic modelling in Python.
Exploratory analysis of Bayesian models with Python
PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian inference. It provides a probabilistic graphical model library for specifying random variables, priors, and likelihood functions, supported by an MCMC sampling engine and variational inference tools to estimate posterior distributions. The framework features a GPU-accelerated inference backend that compiles models into machine code to increase execution speed. It utilizes a backend-agnostic tensor execution model and just-in-time graph compilation to optimize the computation o
PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io
The main features of stan-dev/pystan are: Probabilistic Modeling, Statistical Modeling.
Open-source alternatives to stan-dev/pystan include: pyro-ppl/numpyro — Probabilistic programming powered by JAX for autograd and JIT compilation to GPU/TPU/CPU. pymc-devs/pymc — PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian… arviz-devs/arviz — Exploratory analysis of Bayesian models with Python. pgmpy/pgmpy — Python Toolkit for Causal and Probabilistic Reasoning. jmschrei/pomegranate — Fast, flexible and easy to use probabilistic modelling in Python. accord-net/framework — This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries…