2016/04/16 by Rohin Shah, Emina Torlak, Shah, Rohin +4
Computer Science · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning and Algorithms #Machine Learning and Data Classification #Programming Languages (cs.PL) #cs.PL
paper · pdf · doi:10.48550/arxiv.1604.04729
arxiv created 2016/04/16 · openalex publication_date 2016/04/16 · arxiv updated 2016/04/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Inference algorithms in probabilistic programming languages (PPLs) can be thought of as interpreters, since an inference algorithm traverses a model given evidence to answer a query. As with interpreters, we can improve the efficiency of inference algorithms by compiling them once the model, evidence and query are known. We present SIMPL, a domain specific language for inference algorithms, which uses this idea in order to automatically specialize annotated inference algorithms. Due to the approach of specialization, unlike a traditional compiler, with SIMPL new inference algorithms can be added easily, and still be optimized using domain-specific information. We evaluate SIMPL and show that partial evaluation gives a 2-6x speedup, caching provides an additional 1-1.5x speedup, and generating C code yields an additional 13-20x speedup, for an overall speedup of 30-150x for several inference algorithms and models.