Statically bounded-memory delayed sampling for probabilistic streams
Abstract
Probabilistic programming languages aid developers performing Bayesian inference. These languages provide programming constructs and tools for probabilistic modeling and automated inference. Prior work introduced a probabilistic programming language, ProbZelus, to extend probabilistic programming functionality to unbounded streams of data. This work demonstrated that the delayed sampling inference algorithm could be extended to work in a streaming context. ProbZelus showed that while delayed sampling could be effectively deployed on some programs, depending on the probabilistic model under consideration, delayed sampling is not guaranteed to use a bounded amount of memory over the course of the execution of the program.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 15, 2021
- Source ID
- 10.1145/3485492
Entities
People
- Charles Yuan
- Eric Atkinson
- Guillaume Baudart
- Louis Mandel
- Michael Carbin
Organizations
- IBM Research
- Massachusetts Institute of Technology
- Office of Naval Research
- PSL Research University