1.2.3

• Version release in sync with the published paper version, "Deep Probabilistic Programming". A companion webpage is available here (#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.