The items below are considered high priority for future development, and are at various stages of planning and implementation.
Data-parallel gradient evaluation
Taking advantage of multiple CPU cores to parallelize sampling for larger datasets.
MVNormal is a very commonly used distribution that is currently not supported by Rainier.
Discrete latent variables
Discrete distributions, at least in some cases, with automatic Rao-Blackwellization.
Rainier currently only supports non-centered parameterizations, which is a good default, but automatic reparameterization as in Gorinova et al would be an improvement in some cases.
Mass Matrix adaptation
Currently Rainier's HMC always uses the identity mass matrix. Mass matrix adaptation would improve performance on correlated parameters.
Feel free to file issues at GitHub if something important to you is missing from this list.
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