Modular Runtime Complexity Analysis of Probabilistic While Programs

TL;DR

This paper develops a modular approach for analyzing the average case runtime complexity of probabilistic programs, focusing on a language with sampling and probabilistic choice primitives.

Contribution

It introduces a novel modular analysis method for probabilistic programs, extending techniques from non-probabilistic resource analysis.

Findings

Provides a framework for modular runtime analysis of probabilistic programs

Extends existing resource analysis techniques to probabilistic settings

Enables compositional reasoning about program complexity

Abstract

We are concerned with the average case runtime complexity analysis of a prototypical imperative language endowed with primitives for sampling and probabilistic choice. Taking inspiration from known approaches from to the modular resource analysis of non-probabilistic programs, we investigate how a modular runtime analysis is obtained for probabilistic programs.