Release 0.6.0 · pyro-ppl/numpyro

New Features

Progress bar is available for running parallel MCMC chains.
New samplers:
- BarkerMH - a Metropolis-Hastings sampler that uses a skew-symmetric proposal distribution that depends on the gradient of the potential
- New taylor_proxy for HMCECS sampler. This control variate significantly improves the performance of HMCECS on tall data.
- MixedHMC for mixed discrete and continuous variables
New distributions:
- ProjectedNormal is similar to von Mises and von Mises-Fisher distributions but permits tractable variational inference via reparametrizers
- TruncatedDistribution to truncate over a family of symmetric distributions: Cauchy, Laplace, Logistic, Normal, StudentT
New method Distribution.infer_shapes() for static shape analysis.
New constraints: sphere, positive_ordered_vector, softplus_positive, softplus_lower_cholesky
New transforms: SoftplusTransform, SoftplusLowerCholeskyTransform
New reparameterizer: ProjectedNormalReparam for ProjectedNormal distribution
New obs_mask argument in sample primitive for masked conditioning
New examples:
- Hamiltonian Monte Carlo with Energy Conserving Subsampling
- MCMC Methods for Tall Data

Improve precision for Dirichlet distributions with small concentration #943
Make it easy to use softplus transforms in autoguides #941
Improving compiling time in MCMC samplers - compiling time is 2x faster than previously #924
Reduce memory requirement for AutoLowRankMultivariateNormal.quantiles #921
Example of how to use Distribution.mask #917
Add goodness of fit helpers for testing distributions #916
Enabling sampling with intermediates for ExpandedDistribution #909
Fix DiscreteHMCGibbs to work with multiple chains #908
Fix missing infer key in handlers.lift #892

Thanks @loopylangur, Dominik Straub @dominikstrb, Jeremie Coullon @jeremiecoullon, Ola Rønning @OlaRonning, Lukas Prediger @lumip, Raúl Peralta Lozada @RaulPL, Vitalii Kleshchevnikov @vitkl, Matt Ludkin @ludkinm, and many others for your contributions and feedback!