Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages
This work addresses a computational bottleneck for AI applications using probabilistic programming, offering a practical solution for faster reasoning, though it is incremental as it builds on existing inference algorithms.
The paper tackles the challenge of efficient reasoning on large, complex probabilistic models by introducing the Structured Factured Inference (SFI) framework, which decomposes models into sub-models for targeted inference and achieves accuracy nearly matching exact inference while maintaining the speed benefits of approximate methods.
Reasoning on large and complex real-world models is a computationally difficult task, yet one that is required for effective use of many AI applications. A plethora of inference algorithms have been developed that work well on specific models or only on parts of general models. Consequently, a system that can intelligently apply these inference algorithms to different parts of a model for fast reasoning is highly desirable. We introduce a new framework called structured factored inference (SFI) that provides the foundation for such a system. Using models encoded in a probabilistic programming language, SFI provides a sound means to decompose a model into sub-models, apply an inference algorithm to each sub-model, and combine the resulting information to answer a query. Our results show that SFI is nearly as accurate as exact inference yet retains the benefits of approximate inference methods.