0.2.4

New Features

• NumPyro can be used on Cloud TPUs.
• Deterministic primitive to record deterministic values in a model.
• Mask handler to mask out the log probability of a sample site using a mask array.
• Sample Adaptive MCMC, a non-gradient based sampler that has a high effective sample size per second.
• New normalizing flow: Block neural autoregressive transform.
• Additional auto guides: AutoLowRankMultivariateNormal and AutoBNAFNormal.

New Examples

• Predator-prey model example: uses MCMC to solve the inverse problem of an ODE system.
• Neural transport example: uses a normalizing flow to transform the posterior to a Gaussian-like one, thereby improving mixing rate for HMC/NUTS.

Deprecation / Breaking Changes

• Predictive's get_samples method is deprecated in favor of __call__ method.
• MCMC constrain_fn is renamed to postprocess_fn.

Enhancements and Bug Fixes

• Change the init scale of Auto*Normal guides from 1. to 0.1 - this is helpful for stability during the early training phase.
• Resolve overflow issue with the Poisson sampler.