Edward

+ Added DirichletProcess random variable (555)
+ Added progress bar for inference (546).
+ Improved type support and error messages (561, 563).
+ Miscellaneous bug fixes.

Documentation

+ Added Edward Forum (https://discourse.edwardlib.org)
+ Added Jupyter notebook for all tutorials (520).
+ Added tutorial on linear mixed effects models (539).
+ Added example of probabilistic matrix factorization (557).
+ Improved API styling and reference page (536, 548, 549).
+ Updated website sidebar, including a community page (533, 551).

Acknowledgements
+ Thanks go to Mayank Agrawal (timshell), Siddharth Agrawal (siddharth-agrawal), Lyndon Ollar (lbollar), Christopher Prohm (chmp), Maja Rudolph (mariru).

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

+ Version release in sync with the published paper version, "[Deep Probabilistic Programming](https://arxiv.org/abs/1701.03757)". A companion webpage is available [here](http://edwardlib.org/iclr2017) (510).

Models
+ All support is removed for model wrappers (514, 517).
+ Direct fetching (`sess.run()` and `eval()`) is enabled for `RandomVariable` (503).
+ Index, iterator, and boolean operators are overloaded for `RandomVariable` (515).

Inference
+ Variational inference is added for implicit probabilistic models (491).
+ Laplace approximation uses multivariate normal approximating families (506).
+ Removed need for manually specifying Keras session during inference (490).
+ Recursive graphs are properly handled during inference (500).

Documentation & Examples
+ Probabilistic PCA tutorial is added (499).
+ Dirichlet process with base distribution example is added (508).
+ Bayesian logistic regression example is added (509).

Miscellanea
+ Dockerfile is added (494).
+ Replace some utility functions with TensorFlow's (504, 507).
+ A number of miscellaneous revisions and improvements (e.g., 422, 493, 495).

Acknowledgements
- Thanks go to Mayank Agrawal (timshell), Paweł Biernat (pwl), Tom Diethe (tdiethe), Christopher Prohm (chmp), Maja Rudolph (mariru), SnowMasaya.

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

Models
- Operators are overloaded for `RandomVariable`. For example, this enables `x + y` (445).
- Keras' neural net layers can now be applied directly to `RandomVariable` (483).

Inference
- Generative adversarial networks are implemented, available as `GANInference`. There's a [tutorial](http://edwardlib.org/tutorials/gan) (310).
- Wasserstein GANs are implemented, available as `WGANInference` (448).
- Several integration tests are implemented (487).
- The scale factor argument for `VariationalInference` is generalized to be a tensor (467).
- `Inference` can now work with `tf.Tensor` latent variables and observed variables (488).

Criticism
- A number of miscellaneous improvements are made to `ed.evaluate` and `ed.ppc`. This includes support for checking implicit models and proper Monte Carlo estimates for the posterior predictive density (485).

Documentation & Examples
- [Edward tutorials](http://edwardlib.org/tutorials/) are reorganized in the style of a flattened list (455).
- Mixture density network tutorial is updated to use native modeling language (459).
- Mixed effects model examples are added (461).
- Dirichlet-Categorical example is added (466).
- Inverse Gamma-Normal example is added (475).
- Minor fixes have been made to documentation (437, 438, 440, 441, 454).
- Minor fixes have been made to examples (434).

Miscellanea
- To support both `tensorflow` and `tensorflow-gpu`, TensorFlow is no longer an explicit dependency (482).
- The `ed.tile` utility function is removed (484).
- Minor fixes have been made in the code base (433, 479, 486).

Acknowledgements
- Thanks go to Janek Berger (janekberger), Nick Foti (nfoti), Patrick Foley (patrickeganfoley), Alp Kucukelbir (akucukelbir), Alberto Quirós (bertini36), Ramakrishna Vedantam (vrama91), Robert Winslow (rw).

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 compatible with [TensorFlow 1.0](https://www.tensorflow.org/versions/r1.0/). This provides significantly more distribution support. In addition, Edward now requires TensorFlow 1.0.0-alpha or above (374, 426).

Inference
- Stochastic gradient Hamiltonian Monte Carlo is implemented (415).
- Leapfrog calculation is streamlined in HMC, providing speedups in the algorithm (414).
- Inference now accepts `int` and `float` data types (421).
- Order mismatch of latent variables during MCMC updates is fixed (413).

Documentation & Examples
- Rasch model example is added (410).
- Collapsed mixture model example is added (350).
- Importance weighted variational inference example is updated to use native modeling language.
- Lots of minor improvements to code and documentation (e.g., 409, 418).

Acknowledgements
- Thanks go to Gökçen Eraslan (gokceneraslan), Jeremy Kerfs (jkerfs), Matt Hoffman (matthewdhoffman), Nick Foti (nfoti), Daniel Wadden (dwadden), Shijie Wu (shijie-wu).

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

- Version released in sync with release of the preprint, "[Deep Probabilistic Programming](https://arxiv.org/abs/1701.03757)".

Documentation
- Website documentation and API is improved (381, 382, 383).
- [Gitter channel](https://gitter.im/blei-lab/edward) is added (400).
- Added docstrings to random variables (394).

Miscellaneous
- `copy` is disabled for Queue operations (384).
- All `VariationalInference` methods must use build_loss_and_gradients (385).
- Logging is improved for `VariationalInference` (337).
- Fixed logging issue during inference (391).
- Fixed `copy` function to work with lists of `RandomVariable` (401).
- Fixed bug with Theano `NameError` during inference (395).

Acknowledgements
- Thanks go to Gilles Boulianne (bouliagi), Nick Foti (nfoti), Jeremy Kerfs (jkerfs), Alp Kucukelbir (akucukelbir), John Pearson (jmxpearson), and redst4r.

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

- TensorFlow v0.12.0rc0 and v0.12.0rc1 broke compatibility with Edward (see 315 for more details). For now, users are recommended to use v0.11.0.
- A bug with `KLqp` using the score function gradient estimator is fixed (373).