0.2.1

• @poutine.broadcast is a new effect hadler that allows sample site shapes to be automatically broadcast based on their enclosig iaranges. This makes it very easy to experiment with different models by moving sample sites in and out of iaranges without any manual .expand() changes. See the tensor shapes tutorial for details.
• pyro.optim.PyroLRScheduler makes it easy to use PyTorch learning rate schedulers in Pyro.
• pyro.contrib.autoguide now supports custom name prefixes and has more thorough error messages for name collision. This makes it easier to combine multiple autoguide strategies.
• pyro.ops.newton.newton_step_2d is a fast differentiable optimizer for batched 2-dimensional loss functions that are themselves twice differentiable.
• pyro.contrib.gp.kernels.Coregionalize and pyro.contrib.autoguide.AutoLowRankMultivariateNormal both provide models multivariate data with low-rank plus diagonal covariance.
• TorchDistribution.expand() is more flexible and more PyTorch idiomatic than the older TorchDistribution.expand_by().
• Miscellaneous bugfixes