Online and Interactive Bayesian Inference Debugging

TL;DR

This paper introduces an online, interactive debugging tool for Bayesian inference in probabilistic programming, significantly reducing debugging time and expertise needed, thereby making Bayesian models more accessible.

Contribution

The paper presents a novel debugging framework integrated into development environments that improves efficiency and accessibility in Bayesian inference troubleshooting.

Findings

Reduces debugging time by a significant margin

Eases the debugging process for practitioners with varying expertise

Validated through a user study with 18 experienced participants

Abstract

Probabilistic programming is a rapidly developing programming paradigm which enables the formulation of Bayesian models as programs and the automation of posterior inference. It facilitates the development of models and conducting Bayesian inference, which makes these techniques available to practitioners from multiple fields. Nevertheless, probabilistic programming is notoriously difficult as identifying and repairing issues with inference requires a lot of time and deep knowledge. Through this work, we introduce a novel approach to debugging Bayesian inference that reduces time and required knowledge significantly. We discuss several requirements a Bayesian inference debugging framework has to fulfill, and propose a new tool that meets these key requirements directly within the development environment. We evaluate our results in a study with 18 experienced participants and show that our approach to online and interactive debugging of Bayesian inference significantly reduces time and difficulty on inference debugging tasks.