PyMC3 v3.2 release

News
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  • This version includes two major contributions from our Google Summer of Code 2017 students:
    • Maxim Kochurov @ferrine 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 @bwengals 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.
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