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@neerajprad neerajprad released this Sep 8, 2019 · 9 commits to master since this release


  • Interface Changes to MCMC and SVI: The interface for inference algorithms have been simplified, and is much closer to Pyro. See MCMC and SVI.
  • Multi-chain Sampling for MCMC: There are three options provided: parallel (default), sequential, and vectorized. Currently, parallel method is the fastest among the three.

Breaking changes

  • The primitives param, sample are moved to primitives module. All primities are exposed in numpyro namespace.

New Features


  • In MCMC, we have the option to collect fields other than just the samples such as number of steps or step size, using collect_fields arg in This can be useful when gathering diagnostic information during debugging.
  • diverging field is added to HMCState. This field is useful to detect divergent transitions.
  • Support improper prior through param primitives. e.g.
def model(data):
    loc = numpyro.param('loc', 0.)
    scale = numpyro.param('scale', 0.5, constraint=constraints.positive)
    return numpyro.sample('obs', dist.Normal(loc, scale), obs=data)

Primitives / Effect Handlers

  • module primitive to support JAX style neural network. See VAE example.
  • condition handler for conditioning sample sites to observed data.
  • scale handler for rescaling the log probability score.


JAX optimizers are wrapped in the numpyro.optim module, so that the optimizers can be passed in directly to SVI.



  • predictive utility for vectorized predictions from the posterior predictive distribution.


An experimental autoguide module, with more autoguides to come.

New Examples

Enhancements and Bug Fixes

  • Improve compiling time in MCMC.
  • Better PRNG splitting mechanism in SVI (to avoid reusing PRNG keys).
  • Correctly handle models with dynamically changing distribution constraints. e.g.
def model():
    x = numpyro.sample('x', dist.Uniform(0., 2.))
    y = numpyro.sample('y', dist.Uniform(0., x))  # y's support is not static.
  • Fixes step_size getting NaN in MCMC when it becomes extremely small.
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