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.