  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.
