- There is now one data object to rule them all: a Python dictionary. (see 156)
- Distribution objects can be of arbitrary shape. For example, a 5 x 2 matrix of Normal random variables is declared with `x = Normal([5, 2])`. (see 138)
Documentation
- All of Edward is documented. (see 148)
- Edward now follows [TensorFlow style guidelines](https://www.tensorflow.org/versions/r0.9/how_tos/style_guide.html).
- A tutorial on black box variational inference is available. (see 153)
Miscellaneous
- We now use the special functions and their automatic differentation available in TensorFlow, e.g., `tf.lgamma`, `tf.digamma`, `tf.lbeta`.
- Sampling via NumPy/SciPy is done using a `tf.py_func` wrapper, speeding up sampling and avoiding internal overhead from the previous solution. (see 160)
- Sampling via reparameterizable distributions now follows the convention of `tf.contrib.distributions`. (see 161)
- Fixed bug where a class copy of the `layers` object in `Variational` is done (see 119)