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Release Notes

PyMC 3.3. (Unreleased)

New features

  • Improve NUTS initialization advi+adapt_diag_grad and add jitter+adapt_diag_grad (#2643)
  • Added MatrixNormal class for representing vectors of multivariate normal variables
  • Implemented HalfStudentT distribution
  • New benchmark suite added (see http://pandas.pydata.org/speed/pymc3/)
  • Generalized random seed types
  • Update loo, new improved algorithm (#2730)
  • New CSG (Constant Stochastic Gradient) approximate posterior sampling algorithm (#2544)
  • Michael Osthege added support for population-samplers and implemented differential evolution metropolis (DEMetropolis). For models with correlated dimensions that can not use gradient-based samplers, the DEMetropolis sampler can give higher effective sampling rates. (also see PR#2735)
  • Forestplot supports multiple traces (#2736)
  • Add new plot, densityplot (#2741)

Fixes

  • Fixed compareplot to use loo output.
  • Improved posteriorplot to scale fonts
  • sample_ppc_w now broadcasts
  • df_summary function renamed to summary
  • Add test for model.logp_array and model.bijection (#2724)
  • Fixed sample_ppc and sample_ppc_w to iterate all chains(#2633, #2748)
  • Add Bayesian R2 score (for GLMs) stats.r2_score (#2696) and test (#2729).
  • SMC works with transformed variables (#2749)

PyMC3 3.2 (October 10, 2017)

New features

This version includes two major contributions from our Google Summer of Code 2017 students:

  • Maxim Kochurov extended and refactored the variational inference module. This primarily adds two important classes, representing operator variational inference (OPVI) objects and Approximation objects. These make it easier to extend existing variational classes, and to derive inference from variational optimizations, respectively. The variational module now also includes normalizing flows (NFVI).
  • Bill Engels added an extensive new Gaussian processes (gp) module. Standard GPs can be specified using either Latent or Marginal classes, depending on the nature of the underlying function. A Student-T process TP has been added. In order to accomodate larger datasets, approximate marginal Gaussian processes (MarginalSparse) have been added.

Documentation has been improved as the result of the project's monthly "docathons".

An experimental stochastic gradient Fisher scoring (SGFS) sampling step method has been added.

The API for find_MAP was enhanced.

SMC now estimates the marginal likelihood.

Added Logistic and HalfFlat distributions to set of continuous distributions.

Bayesian fraction of missing information (bfmi) function added to stats.

Enhancements to compareplot added.

QuadPotential adaptation has been implemented.

Script added to build and deploy documentation.

MAP estimates now available for transformed and non-transformed variables.

The Constant variable class has been deprecated, and will be removed in 3.3.

DIC and BPIC calculations have been sped up.

Arrays are now accepted as arguments for the Bound class.

random method was added to the Wishart and LKJCorr distributions.

Progress bars have been added to LOO and WAIC calculations.

All example notebooks updated to reflect changes in API since 3.1.

Parts of the test suite have been refactored.

Fixes

Fixed sampler stats error in NUTS for non-RAM backends

Matplotlib is no longer a hard dependency, making it easier to use in settings where installing Matplotlib is problematic. PyMC will only complain if plotting is attempted.

Several bugs in the Gaussian process covariance were fixed.

All chains are now used to calculate WAIC and LOO.

AR(1) log-likelihood function has been fixed.

Slice sampler fixed to sample from 1D conditionals.

Several docstring fixes.

Contributors

The following people contributed to this release (ordered by number of commits):

Maxim Kochurov [email protected] Bill Engels [email protected] Chris Fonnesbeck [email protected] Junpeng Lao [email protected] Adrian Seyboldt [email protected] AustinRochford [email protected] Osvaldo Martin [email protected] Colin Carroll [email protected] Hannes Vasyura-Bathke [email protected] Thomas Wiecki [email protected] michaelosthege [email protected] Marco De Nadai [email protected] Kyle Beauchamp [email protected] Massimo [email protected] ctm22396 [email protected] Max Horn [email protected] Hennadii Madan [email protected] Hassan Naseri [email protected] Peadar Coyle [email protected] Saurav R. Tuladhar [email protected] Shashank Shekhar [email protected] Eric Ma [email protected] Ed Herbst [email protected] tsdlovell [email protected] zaxtax [email protected] Dan Nichol [email protected] Benjamin Yetton [email protected] jackhansom [email protected] Jack Tsai [email protected] Andrés Asensio Ramos [email protected]

PyMC3 3.1 (June 23, 2017)

New features

Fixes

  • Bound now works for discrete distributions as well.

  • Random sampling now returns the correct shape even for higher dimensional RVs.

  • Use theano Psi and GammaLn functions to enable GPU support for them.

PyMC3 3.0 (January 9, 2017)

We are proud and excited to release the first stable version of PyMC3, the product of more than 5 years of ongoing development and contributions from over 80 individuals. PyMC3 is a Python module for Bayesian modeling which focuses on modern Bayesian computational methods, primarily gradient-based (Hamiltonian) MCMC sampling and variational inference. Models are specified in Python, which allows for great flexibility. The main technological difference in PyMC3 relative to previous versions is the reliance on Theano for the computational backend, rather than on Fortran extensions.

New features

Since the beta release last year, the following improvements have been implemented:

  • Added variational submodule, which features the automatic differentiation variational inference (ADVI) fitting method. Also supports mini-batch ADVI for large data sets. Much of this work was due to the efforts of Taku Yoshioka, and important guidance was provided by the Stan team (specifically Alp Kucukelbir and Daniel Lee).

  • Added model checking utility functions, including leave-one-out (LOO) cross-validation, BPIC, WAIC, and DIC.

  • Implemented posterior predictive sampling (sample_ppc).

  • Implemented auto-assignment of step methods by sample function.

  • Enhanced IPython Notebook examples, featuring more complete narratives accompanying code.

  • Extensive debugging of NUTS sampler.

  • Updated documentation to reflect changes in code since beta.

  • Refactored test suite for better efficiency.

  • Added von Mises, zero-inflated negative binomial, and Lewandowski, Kurowicka and Joe (LKJ) distributions.

  • Adopted joblib for managing parallel computation of chains.

  • Added contributor guidelines, contributor code of conduct and governance document.

Deprecations

  • Argument order of tau and sd was switched for distributions of the normal family:
  • Normal()
  • Lognormal()
  • HalfNormal()

Old: Normal(name, mu, tau) New: Normal(name, mu, sd) (supplying keyword arguments is unaffected).

  • MvNormal calling signature changed: Old: MvNormal(name, mu, tau) New: MvNormal(name, mu, cov) (supplying keyword arguments is unaffected).

We on the PyMC3 core team would like to thank everyone for contributing and now feel that this is ready for the big time. We look forward to hearing about all the cool stuff you use PyMC3 for, and look forward to continued development on the package.

Contributors

The following authors contributed to this release:

Chris Fonnesbeck [email protected] John Salvatier [email protected] Thomas Wiecki [email protected] Colin Carroll [email protected] Maxim Kochurov [email protected] Taku Yoshioka [email protected] Peadar Coyle (springcoil) [email protected] Austin Rochford [email protected] Osvaldo Martin [email protected] Shashank Shekhar [email protected]

In addition, the following community members contributed to this release:

A Kuz [email protected] A. Flaxman [email protected] Abraham Flaxman [email protected] Alexey Goldin [email protected] Anand Patil [email protected] Andrea Zonca [email protected] Andreas Klostermann [email protected] Andres Asensio Ramos Andrew Clegg [email protected] Anjum48 Benjamin Edwards [email protected] Boris Avdeev [email protected] Brian Naughton [email protected] Byron Smith Chad Heyne [email protected] Corey Farwell [email protected] David Huard [email protected] David Stück [email protected] DeliciousHair [email protected] Dustin Tran Eigenblutwurst [email protected] Gideon Wulfsohn [email protected] Gil Raphaelli [email protected] Gogs [email protected] Ilan Man Imri Sofer [email protected] Jake Biesinger [email protected] James Webber [email protected] John McDonnell [email protected] Jon Sedar [email protected] Jordi Diaz Jordi Warmenhoven [email protected] Karlson Pfannschmidt [email protected] Kyle Bishop [email protected] Kyle Meyer [email protected] Lin Xiao Mack Sweeney [email protected] Matthew Emmett [email protected] Michael Gallaspy [email protected] Nick [email protected] Osvaldo Martin [email protected] Patricio Benavente [email protected] Raymond Roberts Rodrigo Benenson [email protected] Sergei Lebedev [email protected] Skipper Seabold [email protected] Thomas Kluyver [email protected] Tobias Knuth [email protected] Volodymyr Kazantsev Wes McKinney [email protected] Zach Ploskey [email protected] akuz [email protected] brandon willard [email protected] dstuck [email protected] ingmarschuster [email protected] jan-matthis [email protected] jason JasonTam22@gmailcom kiudee [email protected] maahnman [email protected] macgyver [email protected] mwibrow [email protected] olafSmits [email protected] paul sorenson [email protected] redst4r [email protected] santon [email protected] sgenoud [email protected] stonebig Tal Yarkoni [email protected] x2apps [email protected] zenourn [email protected]

PyMC3 3.0b (June 16th, 2015)

Probabilistic programming allows for flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source probabilistic programmer framework with an intuitive, readable and concise, yet powerful, syntax that is close to the natural notation statisticians use to describe models. It features next-generation fitting techniques, such as the No U-Turn Sampler, that allow fitting complex models with thousands of parameters without specialized knowledge of fitting algorithms.

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.

Thus, Thomas, Chris and I are pleased to announce that PyMC3 is now in Beta.

Highlights

  • Transforms now automatically applied to constrained distributions
  • Transforms now specified with a transform= argument on Distributions. model.TransformedVar is gone.
  • Transparent missing value imputation support added with MaskedArrays or pandas.DataFrame NaNs.
  • Bad default values now ignored
  • Profile theano functions using model.profile(model.logpt)

Contributors since 3.0a