1.3.0

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 example.
• Added Gibbs sampling (#607). See the unsupervised learning tutorial 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.