1.2.1

• Edward is compatible with TensorFlow 1.0. This provides significantly more distribution support. In addition, Edward now requires TensorFlow 1.0.0-alpha or above (#374, #426).

Inference

• Stochastic gradient Hamiltonian Monte Carlo is implemented (#415).
• Leapfrog calculation is streamlined in HMC, providing speedups in the algorithm (#414).
• Inference now accepts int and float data types (#421).
• Order mismatch of latent variables during MCMC updates is fixed (#413).

Documentation & Examples

• Rasch model example is added (#410).
• Collapsed mixture model example is added (#350).
• Importance weighted variational inference example is updated to use native modeling language.
• Lots of minor improvements to code and documentation (e.g., #409, #418).

Acknowledgements

• Thanks go to Gökçen Eraslan (@gokceneraslan), Jeremy Kerfs (@jkerfs), Matt Hoffman (@matthewdhoffman), Nick Foti (@nfoti), Daniel Wadden (@dwadden), Shijie Wu (@shijie-wu).

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.