1.0.5

• enabled Keras as neural network specification
• samples in variational model can now leverage TensorFlow-based
  samplers and not only SciPy-based samplers
• let user optionally specify sess when using inference
• mean-field variational inference can now take advantage of analytically tractable KL terms for standard normal priors
• data can additionally be a list of np.ndarrays or list of
  tf.placeholders
• added mixture density network as example
• enabled dimensions of distribution output to match with input dimensions
• renamed log_gamma, log_beta, multivariate_log_beta to lgamma
  and lbeta to follow convention in TensorFlow API
• let PointMass be a variational factor
• fixed Multinomial variational factor
• added continuous integration for unit tests