roadmap

docs/roadmap.md

@@ -0,0 +1,33 @@
+---
+id: roadmap
+title: Roadmap
+---
+
+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.
+
+### Multivariate Normal
+
+`MVNormal` is a very commonly used distribution that is currently not supported by Rainier.
+
+### Discrete latent variables
+
+Support `latent` for `Discrete` distributions, at least in some cases, with automatic Rao-Blackwellization.
+
+### Automatic Reparameterization
+
+Rainier currently only supports non-centered parameterizations, which is a good default, but automatic reparameterization as in
+[Gorinova et al](https://arxiv.org/pdf/1906.03028.pdf) 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.
+
+## Feedback
+
+Feel free to file issues at [GitHub](https://github.com/stripe/rainier/issues) if something important to you is missing from this list.
+
+You may also send email with any feedback (good or bad!) to `[email protected]`.

website/sidebars.json

@@ -1,7 +1,7 @@
 {
   "docs": {
     "Overview": ["intro", "priors", "likelihoods", "vectors", "posteriors"],
-    "Installation": ["install", "feedback", "jupyter", "modules"],
+    "Installation": ["install", "jupyter", "roadmap", "modules"],
     "API Reference": ["distributions", "model",  "generator", "real", "vec", "samplers"],
     "Implementation": ["probprog"] 
   }