1.3.4

This version release comes with several new features, alongside a significant push for better documentation, examples, and unit testing.

• ed.KLqp's score function gradient now does more intelligent (automatic) Rao-Blackwellization for variance reduction.
• Automated transformations are enabled for all inference algorithms that benefit from it [tutorial].
• Added Wake-Sleep algorithm (ed.WakeSleep).
• Many minor bug fixes.

Examples

• All Edward examples now rely on the Observations library for data loading (no "official" public release yet; still in alpha).
• Added LSTM language model for text8. (examples/lstm.py)
• Added deep exponential family for modeling topics in NIPS articles. (examples/deep_exponential_family.py)
• Added sigmoid belief network for Caltech-101 silhouettes. (examples/sigmoid_belief_network.py)
• Added stochastic blockmodel on Karate club. (examples/stochastic_block_model.py)
• Added Cox process on synthetic spatial data. (examples/cox_process.py)

Documentation & Testing

• Sealed all undocumented functions and modules in Edward.
• Parser and BibTeX to auto-generate API docs.
• Added unit testing to (most) all Jupyter notebooks.

Acknowledgements

• Thanks go to Matthew Feickert (@matthewfeickert), Alp Kucukelbir (@akucukelbir), Romain Lopez (@romain-lopez), Emile Mathieu (@emilemathieu), Stephen Ra (@stephenra), Kashif Rasul (@kashif), Philippe Rémy (@philipperemy), Charles Shenton (@cshenton), Yuto Yamaguchi (@yamaguchiyuto), @evahlis, @samnolen, @seiyab.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.