1.2.0

• Version released in sync with release of the preprint, "Deep Probabilistic Programming".

Documentation

• Website documentation and API is improved (#381, #382, #383).
• Gitter channel 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.

1.1.6

• 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).

1.1.5

Models

• RandomVariables 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]

Inference

• Inference compositionality is added, enabling algorithms such as Expectation-Maximization and message passing (#330). [Webpage]
• Data subsampling is added, enabling proper local and global variable scaling for stochastic optimization (#327). [Webpage]
• Documentation on inference classes is added. [Webpage]
• 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.

1.1.4

• Small miscellaneous bug fixes.
• Website's API and documentation pages are overhauled.
• A white paper for Edward is released [arXiv:1610.09787].

1.1.3

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.

1.1.2

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/ directory is restructured (#251).
• Many examples with the modeling language are added:
  • Bayesian linear regression
  • Bayesian linear regression with TensorBoard visualization
  • Bayesian neural network
  • Beta-Bernoulli
  • Normal Normal
  • Variational auto-encoder
• Toy demonstrations of several probabilistic programming concepts are
  added:
  • Dirichlet process
  • Persistent randomness
  • Stochastic control flow
  • Stochastic recursion

Miscellaneous

• The TensorFlow dependency is now >=0.10.0.
• Momentum optimizer argument is fixed (#246).

1.1.1

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 (#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 (#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 is revamped.

Examples

• An inference networks tutorial is added.
• A mixture density networks tutorial is added.

Testing

• py.test 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 (#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.

1.1.0

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 #162 (comment))

Documentation

• The website 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 is added.
• Latent space model is added.

1.0.9

• There is now one data object to rule them all: a Python dictionary. (see #156)
• Distribution objects can be of arbitrary shape. For example, a 5 x 2 matrix of Normal random variables is declared with x = Normal([5, 2]). (see #138)

Documentation

• All of Edward is documented. (see #148)
• Edward now follows TensorFlow style guidelines.
• A tutorial on black box variational inference is available. (see #153)

Miscellaneous

• We now use the special functions and their automatic differentation available in TensorFlow, e.g., tf.lgamma, tf.digamma, tf.lbeta.
• Sampling via NumPy/SciPy is done using a tf.py_func wrapper, speeding up sampling and avoiding internal overhead from the previous solution. (see #160)
• Sampling via reparameterizable distributions now follows the convention of tf.contrib.distributions. (see #161)
• Fixed bug where a class copy of the layers object in Variational is done (see #119)

1.0.8

• distributions can now be specified with parameters, simplifying use of inference networks, alternative parameterizations, and much of the internals for developing new inference algorithms; see #126
• TensorFlow session is now a global variable and can simply be accessed with get_session(); see #117
• added Laplace approximation
• added utility function to calculate hessian of TensorFlow tensors