Composing Inference Methods For Probabilistic Models
Probabilistic inference procedures are usually coded painstakingly from scratch, for each target model and each inference algorithm. In this talk, I will show how inference procedures can be posed as program transformations which transform one probabilistic model into another probabilistic model. These transformations allow us to generate programs which express exact and approximate inference, and allow us to compose multiple inference procedures over a single model. The resulting inference procedure runs in time comparable to a handwritten procedure.
Rob Zinkov is a Research Scientist at Indiana University working with Chung-chieh Shan on probabilistic programming and Bayesian inference.