PyMC is an open-source probabilistic programming library in Python, designed for Bayesian statistical modeling and inference. Built on top of Aesara, PyMC allows users to construct complex models using intuitive syntax and powerful backends for scalable and efficient computation. PyMC is ideal for research, machine learning, and data science applications that require uncertainty quantification and complex hierarchical modeling.

Features of PyMC:

Intuitive model specification using standard Python syntax.
Support for advanced inference methods like NUTS (No-U-Turn Sampler), HMC, and variational inference.
Flexible modeling of complex hierarchical Bayesian models.
Integration with popular scientific Python libraries such as NumPy, pandas, and ArviZ.
Supports GPU acceleration and symbolic differentiation via Aesara.
Actively developed with a vibrant open-source community and extensive documentation.
Open-source and licensed under the Apache License 2.0.
Ideal for use in research, epidemiology, social science, finance, and engineering domains.

To check Pymc3 version:

$ sudo su

$ source pymc-env/bin/activate

pip show pymc3

Disclaimer: PyMC is open-source software distributed under the Apache License 2.0. It is independently developed and maintained by a community of contributors and is not affiliated with any commercial entities unless explicitly stated. The software is provided "as is," without warranties or guarantees of any kind. Users are responsible for their use of the software and should ensure compliance with applicable laws and licenses.

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