# Fast-PGM: Fast Probabilistic Graphical Model Learning and Inference

arXiv: https://arxiv.org/abs/2405.15605 · HTML: https://mlsanity.com/paper/2405.15605/

## 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 (most cited first, 9 of 9)

- [Causal-learn: Causal Discovery in Python](https://mlsanity.com/paper/2307.16405/) (2023)
- [Loopy Belief Propagation for Approximate Inference: An Empirical Study](https://mlsanity.com/paper/1301.6725/) (2013)
- [A fast PC algorithm for high dimensional causal discovery with multi-core PCs](https://mlsanity.com/paper/1502.02454/) (2015)
- [Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package](https://mlsanity.com/paper/1406.7648/) (2014)
- [Graph Markov Network for Traffic Forecasting with Missing Data](https://mlsanity.com/paper/1912.05457/) (2019)
- [Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks](https://mlsanity.com/paper/1304.1504/) (2013)
- [Fast Parallel Exact Inference on Bayesian Networks: Poster](https://mlsanity.com/paper/2212.04241/) (2022)
- [Simulation Approaches to General Probabilistic Inference on Belief Networks](https://mlsanity.com/paper/1304.1526/) (2013)
- [Fast Parallel Bayesian Network Structure Learning](https://mlsanity.com/paper/2212.04259/) (2022)

## Built on by (most cited first, 2 of 2)

- [Regulating Model Reliance on Non-Robust Features by Smoothing Input Marginal Density](https://mlsanity.com/paper/2407.04370/) (2024)
- [FastBO: Fast HPO and NAS with Adaptive Fidelity Identification](https://mlsanity.com/paper/2409.00584/) (2024)

## 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.