1.5.0

New features

• New Tutorials:
  • Deep Generative Modeling with Single Cell Data
  • Conditional Variational Autoencoder
  • Forecasting with Dynamic Linear Models
• New distributions and transforms:
  • OrderedLogistic distribution and OrderedTransform
  • ConditionalMatrixExponential normalizing flow
  • ConditionalSplineAutoregressive normalizing flow
• contrib.forecast now supports HaarReparam, DiscreteCosineReparam, and poutine.trace to record posterior samples of latent variables.
• CompartmentalModel now supports a .finalize() method to add likelihoods that couple states across time.
• Integration with Funsor, an experimental intermediate language for probabilistic programming
  • pyro.contrib.funsor is a new backend for Pyro that aims to simplify the implementations of Pyro's most powerful inference engines. For details, see: tutorial 1, tutorial 2, example usage with pyroapi
  • poutine.collapse and pyro.barrier provide experimental support for collapsing conjugate fragments within existing inference algorithms, using Funsor under the hood.

Breaking changes

• Require PyTorch 1.6.
• Drop support for Python 3.5; require Python 3.6+.
• Zero inflated distributions changed interface. #2643

Bug fixes & performance tweaks

• pyro.factor statements are now allowed in guides without warning. #2664
• Fix model-directed subsampling in autoguides. #2638
• Fix sample shape bug in LKJCorrCholesky distribution. #2617
• Speed up log-matmul-exp operations in discrete enumeration and DiscreteHMM. #2640
• Fix potential_fn issues in MCMC. #2591