Probabilistic graphical models (PGMs) serve as a powerful framework for modeling complex systems with uncertainty and extracting valuable insights from data. However, users face challenges when applying PGMs to their problems in terms of efficiency and usability. This paper presents Fast-PGM, an efficient and open-source library for PGM learning and inference. Fast-PGM supports comprehensive tasks on PGMs, including structure and parameter learning, as well as exact and approximate inference, and enhances efficiency of the tasks through computational and memory optimizations and parallelization techniques. Concurrently, Fast-PGM furnishes developers with flexible building blocks, furnishes learners with detailed documentation, and affords non-experts user-friendly interfaces, thereby ameliorating the usability of PGMs to users across a spectrum of expertise levels. The source code of Fast-PGM is available at https://github.com/jjiantong/FastPGM.

翻译：概率图模型（PGMs）作为一种强大的框架，可用于对具有不确定性的复杂系统进行建模，并从数据中提取有价值的洞见。然而，用户在将PGMs应用于实际问题时，在效率和易用性方面面临挑战。本文提出了Fast-PGM，一个用于PGM学习与推理的高效开源库。Fast-PGM支持PGMs上的全面任务，包括结构与参数学习，以及精确与近似推理，并通过计算与内存优化以及并行化技术提升了这些任务的效率。同时，Fast-PGM为开发者提供了灵活的构建模块，为学习者提供了详细的文档，并为非专家用户提供了友好的界面，从而改善了PGMs对不同专业水平用户的易用性。Fast-PGM的源代码可在 https://github.com/jjiantong/FastPGM 获取。