Composing inference algorithms as program transformations

Understand

Probabilistic inference procedures are usually coded painstakingly from scratch, for each target model and each inference algorithm.

• We reduce this effort by generating inference procedures from models automatically.
• We make this code generation modular by decomposing inference algorithms into reusable program-to-program transformations.
• These transformations perform exact inference as well as generate probabilistic programs that compute expectations, densities, and MCMC samples.