Automatic Alignment of Sequential Monte Carlo Inference in Higher-Order Probabilistic Programs

TL;DR

This paper introduces a static analysis method using 0-CFA to automatically align sequential Monte Carlo inference in higher-order probabilistic programs, improving runtime and accuracy.

Contribution

It presents a novel static analysis approach for automatic alignment in higher-order probabilistic programming, addressing synchronization issues in SMC inference.

Findings

Significant decrease in runtime for phylogenetic models

Increase in inference accuracy

Effective static analysis for program alignment

Abstract

Probabilistic programming is a programming paradigm for expressing flexible probabilistic models. Implementations of probabilistic programming languages employ a variety of inference algorithms, where sequential Monte Carlo methods are commonly used. A problem with current state-of-the-art implementations using sequential Monte Carlo inference is the alignment of program synchronization points. We propose a new static analysis approach based on the 0-CFA algorithm for automatically aligning higher-order probabilistic programs. We evaluate the automatic alignment on a phylogenetic model, showing a significant decrease in runtime and increase in accuracy.