Industrial impact with Probabilistic Programming

Unlocking industrial innovation with uncertainty engineering

We investigate how a Probabilistic Programming (PP) framework can impact industrial engineering.

By addressing complex problems with inherent uncertainty, PP offers solutions for:

• Inference Problems. Reasoning about system states from partial observations, with applications ranging from anomaly detection to root cause analysis.

• Inverse Problems. Estimating unknown parameters from noisy data, a critical task from robotics to image reconstruction.

• Optimization Problem. Discovering optimal solutions under uncertainty, benefiting supply chain optimization, resource allocation, and scheduling.

By leveraging PP, industrial engineers can unlock new insights, enhance decision-making, and drive innovation in manufacturing, logistics, and other critical domains.

The full report is available:

Download here (pdf, 1.7 MB)