Edward

Models
- `RandomVariable`s now accept an optional `value` argument, enabling use of random variables that don't currently have sampling such as `Poisson` (326).
- Documentation on model compositionality is added. [[Webpage]](http://edwardlib.org/api/model-compositionality)

Inference
- Inference compositionality is added, enabling algorithms such as Expectation-Maximization and message passing (330). [[Webpage]](http://edwardlib.org/api/inference-compositionality)
- Data subsampling is added, enabling proper local and global variable scaling for stochastic optimization (327). [[Webpage]](http://edwardlib.org/api/inference-data-subsampling)
- Documentation on inference classes is added. [[Webpage]](http://edwardlib.org/api/inference-compositionality)
- `VariationalInference` has new defaults for a TensorFlow variable list as argument (336).
- Type and shape checking is improved during `__init__` of `Inference`.

Miscellaneous
- Fixed an issue where a new Div node is created every Monte Carlo update (318).
- Travis build is now functioning properly (324).
- Coveralls is now functioning properly (342).
- `tf.placeholder` can now be used instead of `ed.placeholder`.
- Website tutorials, documentation, and API are generally more polished.
- Fixed an issue where computation was incorrectly shared among inferences (348).
- `scipy` is now an optional rather than mandatory dependency (344).

Deprecated Features

**NOTE**: Several features in Edward are now deprecated (344):
- model wrappers, including `PythonModel`, `PyMC3Model`, and `StanModel`—in favor of Edward's native language;
- the `edward.stats` module—in favor of random variables in `edward.models`;
- `MFVI`—in favor of `KLqp`;
- `ed.placeholder`—in favor of TensorFlow's `tf.placeholder`.

Edward will continue their support for one or two more versions. They will be removed in some future release.

Acknowledgements
- Thanks go to Alp Kucukelbir (akucukelbir), Dawen Liang (dawenl), John Pearson (jmxpearson), Hayate Iso (isohyt), Marmaduke Woodman (maedoc), and Matthew Hoffman (matthewdhoffman).

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

- Small miscellaneous bug fixes.
- Website's API and documentation pages are overhauled.
- A white paper for Edward is released [[arXiv:1610.09787]](https://arxiv.org/abs/1610.09787).

Models
- New random variables and methods are added (256, 274). For example, random variables such as `Mixture`, `QuantizedDistribution`, `WishartCholesky`, and methods such as `survival_function()`.
- Random variables and methods are now automatically generated from `tf.contrib.distributions` (276). Edward random variables are minimal and adapt to the TensorFlow version.

Inference

**Inference**
- The API is generalized to enable more fine-grained control (253, 259, 260).

**Monte Carlo**
- Significant infrastructure for Monte Carlo is added (254, 255). This makes it easy to develop new Monte Carlo methods.
- Metropolis-Hastings is implemented (255)
- Hamiltonian Monte Carlo is implemented (269).
- Stochastic gradient Langevin dynamics is implemented (272).

**Variational inference**
- Black box-style methods are refactored internally (249).

Documentation
- The website tutorials are placed in a directory and have clean links (263, 264).
- Initial progress is made on iPython notebook versions of the tutorials (261).
- The website API is revamped (268). Everything is now LaTeX-sourced, and the Delving In page is moved to the frontpage of the API.

Miscellaneous
- Printing behavior of random variables is changed (276).
- `edward.criticisms` is its own subpackage (258).
- The TensorFlow dependency is now `>=0.11.0rc0` (274).

Acknowledgements
- Thanks go to Alp Kucukelbir (akucukelbir), Bhargav Srinivasa (bhargavvader), and Justin Bayer (bayerj).

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

Functionality
- A new modeling language is added, which exposes model structure to the user. This enables development of both model-specific and generic inference algorithms (239).
- All of inference and criticism is updated to support the new language and also be backward-compatible with the model wrappers (239).

Documentation
- All of the website is updated to reflect the new modeling language (252).
- Several existing tutorials now use the modeling language instead of a model wrapper (252).

Examples
- The [`examples/`](https://github.com/blei-lab/edward/tree/f3fc5400230d9531af339bb442cd6c8f085657da/examples/) directory is restructured (251).
- Many examples with the modeling language are added:
- [Bayesian linear regression](https://github.com/blei-lab/edward/tree/f3fc5400230d9531af339bb442cd6c8f085657da/examples/bayesian_linear_regression.py)
- [Bayesian linear regression with TensorBoard visualization](https://github.com/blei-lab/edward/tree/f3fc5400230d9531af339bb442cd6c8f085657da/examples/bayesian_linear_regression_tensorboard.py)
- [Bayesian neural network](https://github.com/blei-lab/edward/tree/f3fc5400230d9531af339bb442cd6c8f085657da/examples/bayesian_nn.py)
- [Beta-Bernoulli](https://github.com/blei-lab/edward/tree/f3fc5400230d9531af339bb442cd6c8f085657da/examples/beta_bernoulli.py)
- [Normal Normal](https://github.com/blei-lab/edward/tree/f3fc5400230d9531af339bb442cd6c8f085657da/examples/normal_normal.py)
- [Variational auto-encoder](https://github.com/blei-lab/edward/tree/f3fc5400230d9531af339bb442cd6c8f085657da/examples/vae.py)
- Toy demonstrations of several probabilistic programming concepts are
added:
- [Dirichlet process](https://github.com/blei-lab/edward/tree/f3fc5400230d9531af339bb442cd6c8f085657da/examples/pp_dirichlet_process.py)
- [Persistent randomness](https://github.com/blei-lab/edward/tree/f3fc5400230d9531af339bb442cd6c8f085657da/examples/pp_persistent_randomness.py)
- [Stochastic control flow](https://github.com/blei-lab/edward/tree/f3fc5400230d9531af339bb442cd6c8f085657da/examples/pp_stochastic_control_flow.py)
- [Stochastic recursion](https://github.com/blei-lab/edward/tree/f3fc5400230d9531af339bb442cd6c8f085657da/examples/pp_stochastic_recursion.py)

Miscellaneous
- The TensorFlow dependency is now `>=0.10.0`.
- Momentum optimizer argument is fixed (246).

Functionality
- The API for inference and criticism is changed. It is a more intuitive interface that allows for multiple sets of latent variables (192).
- The API for variational models is changed (237). The user must explicitly define the parameters that he or she wishes to train; this allows for more flexibility in how to initialize and train variational parameters.
- `edward.models` is refactored to incorporate all random variables in [`tf.contrib.distributions`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/distributions/python/ops) (237). This speeds up computation, is more robust, and supports additional distributions and distribution methods.
- `edward.stats` is refactored to have its main internals reside in [`tf.contrib.distributions`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/distributions/python/ops) (238). This speeds up computation, is more robust, and supports additional distributions and distribution methods.

Documentation
- All of the website is updated to reflect the new API changes.
- The [contributing page](http://edwardlib.org/contributing) is revamped.

Examples
- An [inference networks tutorial](http://edwardlib.org/tut_inference_networks) is added.
- A [mixture density networks tutorial](http://edwardlib.org/tut_mixture_density_network) is added.

Testing
- [`py.test`](http://pytest.org) is now the testing tool of choice.
- Code now follows all of PEP8, with the exception of two-space indenting following [TensorFlow's style guide](https://www.tensorflow.org/versions/master/how_tos/style_guide.html) (214, 215, 216, 217, 218, 219, 220, 221, 223, 225, 227, 228, 229, 230).
- Travis automates checking for PEP8.
- (minimal) Tensorboard support is added. Specifically, one can now visualize the computational graph used during inference.

Miscellaneous
- The TensorFlow dependency is now `>=0.10.0rc0`.
- `ed.__version__` displays Edward's version.
- `ed.set_seed()` is more robust, checking to see if any random ops were created prior to setting the seed.

Functionality
- Three ways to read data are supported, enabling the range from storing data in memory within TensorFlow's computational graph to manually feeding data to reading data from files. (see 170)
- Support for Python 3 is added.
- The naming scheme for various attributes is made consistent. (see https://github.com/blei-lab/edward/pull/162issuecomment-232517072)

Documentation
- The [website](http://edwardlib.org) is given a complete overhaul, now with getting started and delving in pages, in-depth tutorials, and an API describing the design of Edward and autogenerated doc for each function in Edward. (see 149)

Examples
- [Importance-weighted variational inference](examples/iwvi.py) is added.
- [Latent space model](examples/latent_space_model.py) is added.