Edward

+ Added automatic posterior approximations in variational inference (775).
+ Added use of `tf.GraphKeys.REGULARIZATION_LOSSES` to variational inference (813).
+ Added multinomial classification metrics (743).
+ Added utility function to assess conditional independence (791).
+ Added custom metrics in evaluate.py (809).
+ Minor bug fixes, including automatic transformations (808); ratio inside `ed.MetropolisHastings` (806).

Acknowledgements

+ Thanks go to Baris Kayalibay (bkayalibay), Christopher Lovell (christopherlovell), David Moore (davmre), Kris Sankaran (krisrs1128), Manuel Haussmann (manuelhaussmann), Matt Hoffman (matthewdhoffman), Siddharth Agrawal (siddharth-agrawal), William Wolf (cavaunpeu), gfeldman.

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

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]](http://edwardlib.org/tutorials/automated-transformations).
+ Added Wake-Sleep algorithm ([`ed.WakeSleep`](http://edwardlib.org/api/ed/WakeSleep)).
+ Many minor bug fixes.

Examples

+ All Edward examples now rely on the [Observations](https://github.com/edwardlib/observations) library for data loading (no "official" public release yet; still in alpha).
+ Added LSTM language model for text8. ([`examples/lstm.py`](https://github.com/blei-lab/edward/blob/2402498511588d61c82c6c1648b63252da85ff8b/examples/lstm.py))
+ Added deep exponential family for modeling topics in NIPS articles. ([`examples/deep_exponential_family.py`](https://github.com/blei-lab/edward/blob/2402498511588d61c82c6c1648b63252da85ff8b/examples/deep_exponential_family.py))
+ Added sigmoid belief network for Caltech-101 silhouettes. ([`examples/sigmoid_belief_network.py`](https://github.com/blei-lab/edward/blob/2402498511588d61c82c6c1648b63252da85ff8b/examples/sigmoid_belief_network.py))
+ Added stochastic blockmodel on Karate club. ([`examples/stochastic_block_model.py`](https://github.com/blei-lab/edward/blob/2402498511588d61c82c6c1648b63252da85ff8b/examples/stochastic_block_model.py))
+ Added Cox process on synthetic spatial data. ([`examples/cox_process.py`](https://github.com/blei-lab/edward/blob/2402498511588d61c82c6c1648b63252da85ff8b/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.

+ Edward is updated to require a TensorFlow version of at least 1.2.0rc0.
+ Miscellaneous bug fixes and revisions.

Acknowledgements

+ Thanks go to Joshua Engelman (jengelman), Matt Hoffman (matthewdhoffman), Kashif Rasul (kashif).

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

+ More TensorBoard support, including default summaries. See the [tutorial](http://edwardlib.org/tutorials/tensorboard) (598, 654, 653).
+ A [batch training tutorial](http://edwardlib.org/tutorials/batch-training) is added.
+ Improved training of Wasserstein GANs via penalty (626).
+ Fixed error in sampling for `DirichletProcess` (652).
+ Miscellaneous bug fixes, documentation, and speed ups.

Acknowledgements

+ Thanks go to Janek Berger (janekberger), Ian Dewancker (iandewancker) Patrick Foley (patrickeganfoley), Nitish Joshi (nitishjoshi25), Akshay Khatri (akshaykhatri639), Sean Kruzel (closedLoop), Fritz Obermeyer (fritzo), Lyndon Ollar (lbollar), Olivier Verdier (olivierverdier), KonstantinLukaschenko, meta-inf.

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

+ Fixed error in 1.3.0 when importing conjugacy submodule.

Edward requires a TensorFlow version of at least 1.1.0rc0. This includes several breaking API changes:
+ All Edward random variables use English keyword arguments instead of Greek. For example, `Normal(loc=0.0, scale=1.0)` replaces the older syntax of `Normal(mu=0.0, sigma=1.0)`.
+ `MultivariateNormalCholesky` is renamed to `MultivariateNormalTriL`.
+ `MultivariateNormalFull` is removed.
+ `rv.get_batch_shape()` is renamed to `rv.batch_shape`.
+ `rv.get_event_shape()` is renamed to `rv.event_shape`.

Model

+ Random variables accept an optional `sample_shape` argument. This lets its associated tensor to represent more than a single sample (591).
+ Added a `ParamMixture` random variable. It is a mixture of random variables where each component has the same distribution (592).
+ `DirichletProcess` has persistent states across calls to `sample()` (565, 575, 583).

Inference

+ Added conjugacy & symbolic algebra. This includes a `ed.complete_conditional` function (588, 605, 613). See [a Beta-Bernoulli](https://github.com/blei-lab/edward/blob/ec45bad40312683df46ead36cd6076b02fb887cf/examples/beta_bernoulli_conjugate.py) example.
+ Added Gibbs sampling (607). See the [unsupervised learning tutorial](http://edwardlib.org/tutorials/unsupervised) for a demo.
+ Added `BiGANInference` for adversarial feature learning (597).
+ `Inference`, `MonteCarlo`, `VariationalInference` are abstract classes, preventing instantiation (582).

Miscellaneous

+ A more informative message appears if the TensorFlow version is not supported (572).
+ Added a `shape` property to random variables. It is the same as `get_shape()`.
+ Added `collections` argument to random variables(609).
+ Added `ed.get_blanket` to get Markov blanket of a random variable (590).
+ `ed.get_dims` and `ed.multivariate_rbf` utility functions are removed.
+ Miscellaneous bug fixes and speed ups (e.g., 567, 596, 616).

Acknowledgements

+ Thanks go to Robert DiPietro (rdipietro), Alex Lewandowski (AlexLewandowski), Konstantin Lukaschenko (KonstantinLukaschenko) Matt Hoffman (matthewdhoffman), Jan-Matthis Lückmann (jan-matthis), Shubhanshu Mishra (napsternxg), Lyndon Ollar (lbollar), John Reid (johnreid), Phdntom.

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