1.2.2

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 (#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 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.