Fast-PGM: Fast Probabilistic Graphical Model Learning and Inference
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 47 references that are in the map. First posted on arXiv 24 May 2024.
Builds on
9 of 47 references.
References · 9
- 2023Causal-learn: Causal Discovery in Pythoncited by 109
- 2013Loopy Belief Propagation for Approximate Inference: An Empirical Studycited by 76
- 2015A fast PC algorithm for high dimensional causal discovery with multi-core PCscited by 45
- 2014Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Packagecited by 19
- 2019Graph Markov Network for Traffic Forecasting with Missing Datacited by 14
- 2013Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networkscited by 4
- 2022Fast Parallel Exact Inference on Bayesian Networks: Postercited by 4
- 2013Simulation Approaches to General Probabilistic Inference on Belief Networkscited by 3
- 2022Fast Parallel Bayesian Network Structure Learningcited by 3
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Citing papers per year: 2024: 2.
2 papers.