Accelerating Inference: towards a full Language, Compiler and Hardware stack by Shawn Hershey, Jeff Bernstein, Bill Bradley, Andrew Schweitzer, Noah Stein, Theo Weber, Ben Vigoda.
Abstract:
We introduce Dimple, a fully open-source API for probabilistic modeling. Dimple allows the user to specify probabilistic models in the form of graphical models, Bayesian networks, or factor graphs, and performs inference (by automatically deriving an inference engine from a variety of algorithms) on the model. Dimple also serves as a compiler for GP5, a hardware accelerator for inference.
From the introduction:
Graphical models alleviate the complexity inherent to large dimensional statistical models (the so-called curse of dimensionality) by dividing the problem into a series of logically (and statistically) independent components. By factoring the problem into subproblems with known and simple interdependencies, and by adopting a common language to describe each subproblem, one can considerably simplify the task of creating complex Bayesian models. Modularity can be taken advantage of further by leveraging this modeling hierarchy over several levels (e.g. a submodel can also be decomposed into a family of sub-submodels). Finally, by providing a framework which abstracts the key concepts underlying classes of models, graphical models allow the design of general algorithms which can be efficiently applied across completely different fields, and systematically derived from a model description.
Suggestive of sub-models of merging?
I first saw this in a tweet from Stefano Bertolo.