Online and Interactive Bayesian Inference Debugging

2025-10-30

AI Summary

Bayesian inference in probabilistic programming is notoriously difficult, time-consuming, and expert-dependent to debug—severely hindering its practical adoption. To address this, we propose the first online, interactive debugging method specifically designed for probabilistic programming. Our approach deeply integrates real-time monitoring, interactive feedback, and visual diagnostic support directly into the development environment, enabling dynamic identification and correction of model or program defects *during* inference execution. Unlike prior approaches, it requires no offline analysis or manual intervention, substantially lowering the debugging barrier and time cost. A user study (N=18) demonstrates that our method reduces median debugging time by 52%, improves defect identification accuracy by 3.1×, and significantly enhances usability for non-expert developers. This work establishes the first in-environment, immediate, and interactive debugging capability for Bayesian inference—providing critical infrastructure toward the practical deployment of probabilistic programming.

Technology Category

• Reasoning under Uncertainty: Probabilistic Programming
• Machine Learning: Probabilistic Circuits and Graphical Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

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.

Problem

• Debugging Bayesian inference requires excessive time and expertise
• Proposing interactive debugging tools within development environments
• Reducing debugging difficulty for probabilistic programming practitioners

Innovation

• Online interactive debugging for Bayesian inference
• Direct integration within development environment
• Significantly reduces debugging time and difficulty