Release 0.2.1 · pyro-ppl/numpyro

Breaking changes

Code reorganization - numpyro.mcmc is moved to numpyro.infer.mcmc but all major classes are exposed in the numpyro.infer module.
rng argument to many classes and the seed handler has been more accurately renamed to rng_key.
Deprecated functions that formed the old interface like mcmc and svi have been removed.

Improved turning condition for NUTS that results in much higher effective sample size for many models.
A numpyro.plate context manager, which records conditional independence information in the trace and does automatic broadcasting, like in Pyro.
Inclusion of AutoMultivariateNormal, AutoLaplaceApproximation to the autoguide module.
More distributions like LowRankMultivariateNormal, LKJ, BetaBinomial, GammaPoisson, ZeroInflatedPoisson, and OrderedLogistic.
More transforms: MultivariateAffineTransform, InvCholeskyTransform, OrderedTransform.
A numpyro.compat module that supports the pyro generic API for modeling and inference that can dispatch to multiple Pyro backends.
Inclusion of Independent distribution and Distribution.to_event method to convert independent batch dimensions to dependent event dimensions. See the Pyro tutorial on tensor shapes for more details.
A Predictive utility for generating samples from prior models, predictions from models using SVI's guide, or posterior samples from MCMC.
A log_likelihood utility function that can compute the log likelihood for observed data by conditioning latent sites to values from the posterior distribution.
New ClippedAdam optimizer to prevent exploding gradients.
New RenyiELBO loss for Renyi divergence variational inference and importance weighted variational inference.

MCMC does not throw an error on models with no latent sites.
numpyro.seed handler can be used as a context manager like:
```
 with numpyro.seed(rng_seed=1):
     ...
```
Utilities to enable validation checks for distributions, set host device count, and platform.
More efficient sampling from Binomial / Multinomial distributions.
The evidence lower bound loss for SVI is now a class called ELBO.
Add energy field to HMCState, which is used to compute Bayesian Fraction of Missing Information for diagnostics.
Add init_strategy arg to HMC/NUTS classes, which allows users select various initialization strategies.