Statically Bounded-Memory Delayed Sampling for Probabilistic Streams

by   Eric Atkinson, et al.

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. In this paper, we present conditions on a probabilistic program's execution under which delayed sampling will execute in bounded memory. The two conditions are dataflow properties of the core operations of delayed sampling: the m-consumed property and the unseparated paths property. A program executes in bounded memory under delayed sampling if, and only if, it satisfies the m-consumed and unseparated paths properties. We propose a static analysis that abstracts over these properties to soundly ensure that any program that passes the analysis satisfies these properties, and thus executes in bounded memory under delayed sampling.


Reactive Probabilistic Programming

Synchronous reactive languages were introduced for designing and impleme...

Program Analysis of Probabilistic Programs

Probabilistic programming is a growing area that strives to make statist...

Automated learning with a probabilistic programming language: Birch

This work offers a broad perspective on probabilistic modeling and infer...

An Introduction to Probabilistic Programming

This document is designed to be a first-year graduate-level introduction...

Linear Models of Computation and Program Learning

We consider two classes of computations which admit taking linear combin...

Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs

We introduce a novel sampling algorithm for Bayesian inference on impera...

C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching

Lightweight, source-to-source transformation approaches to implementing ...