This is the first major update to PyMC 3 since its initial release. Highlights of this release include:

• Gaussian Process submodule
• Much improved variational inference support that includes:
  • Stein Variational Gradient Descent
  • Minibatch processing
  • Additional optimizers, including ADAM
  • Experimental operational variational inference (OPVI)
  • Full-rank ADVI
• MvNormal supports Cholesky Decomposition now for increased speed and numerical stability.
• NUTS implementation now matches current Stan implementation.
• Higher-order integrators for HMC
• Elliptical slice sampler is now available
• Added Approximation class and the ability to convert a sampled trace into an approximation via its Empirical subclass.
• Add MvGaussianRandomWalk and MvStudentTRandomWalk distributions.

This is the first major release of PyMC3. A number of major changes since splitting from the PyMC2 project include:

• Added gradient-based MCMC samplers: Hamiltonian MC (HMC) and No-U-Turn Sampler (NUTS)
• Automatic gradient calculations using Theano
• Convenient generalized linear model specification using Patsy formulae
• Parallel sampling via multiprocessing
• New model specification using context managers
• New Automatic Differentiation Variational InferenceAVDI (ADVI) allowing faster sampling than HMC for some problems.
• Mini-batch ADVI

Sixth release candidate of PyMC3 3.0.

Fifth release candidate of PyMC3 3.0.

Fourth release candidate of PyMC3 3.0.

Updating the release tag. and this is a full release of release candidate 3.

Second release candidate of PyMC3 3.0.

First release candidate of PyMC3 3.0.

PyMC3 has recently seen rapid development. With the addition of two new major features: automatic transforms and missing value imputation, PyMC3 has become ready for wider use. PyMC3 is now refined enough that adding features is easy, so we don’t expect adding features in the future will require drastic changes.

It has also become user friendly enough for a broader audience. Automatic transformations mean NUTS and find_MAP work with less effort, and friendly error messages mean its easy to diagnose problems with your model.