© 2020, The PyMC Development Team
In this post we’d like to make a major announcement about where PyMC is headed, how we got here, and what our reasons for this direction are.
TL;DR: PyMC3 on Theano with the new JAX backend is the future, PyMC4 based on TensorFlow Probability…
Posted by Chris Fonnesbeck on behalf of the PyMC development team
This week featured the release of PyMC 3.7, which includes a slew of bug fixes and enhancements to help make building and fitting Bayesian models easier and more robust than ever. …
Posted by Chris Fonnesbeck on behalf of the PyMC development team
The PyMC development team is proud to announce the release of version 3.5 today. This version features several usability enhancements, so we recommend this update to all users. The implementation of parallel sampling has been refactored to be more…
PyMC3 is an open-source library for Bayesian statistical modeling and inference in Python, implementing gradient-based Markov chain Monte Carlo, variational inference, and other approximation methods. These algorithms currently rely on Theano for computation, specifically for providing gradients.
Posted by Chris Fonnesbeck on behalf of the PyMC development team
Since the…
