Releases: pymc-devs/pymc
v3.0 Final
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 thanHMC
for some problems. - Mini-batch ADVI
v3.0 Release Candidate 6
Sixth release candidate of PyMC3 3.0.
v3.0 Release Candidate 5
Fifth release candidate of PyMC3 3.0.
v3.0 Release Candidate 4
Fourth release candidate of PyMC3 3.0.
v3.0.rc3_full
Updating the release tag. and this is a full release of release candidate 3.
v3.0 Release Candidate 2
Second release candidate of PyMC3 3.0.
v3.0 Release Candidate 1
First release candidate of PyMC3 3.0.
v3.0beta
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.