Accelerating Inference: towards a full Language, Compiler and Hardware stack

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.

References in corpus (1)

• Church: a language for generative models

Cited by in corpus (5)

• A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
• BayesPy: Variational Bayesian Inference in Python
• MCMC with Strings and Branes: The Suburban Algorithm
• MCMC with Strings and Branes: The Suburban Algorithm (Extended Version)
• Applications of Probabilistic Programming (Master's thesis, 2015)