Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.17025 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915868814868480 |
|---|---|
| author | Gaudillo, Joverlyn Astrologo, Nicole Stella, Fabio Acerbi, Enzo Canonaco, Francesco |
| author_facet | Gaudillo, Joverlyn Astrologo, Nicole Stella, Fabio Acerbi, Enzo Canonaco, Francesco |
| contents | A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool for this task. BNs require constructing a structure of dependencies among variables and learning the parameters that govern these relationships. These tasks, referred to as structural learning and parameter learning, are actively investigated by the research community, with several algorithms proposed and no single method having established itself as standard. A wide range of software, tools, and packages have been developed for BNs analysis and made available to academic researchers and industry practitioners. As a consequence of having no one-size-fits-all solution, moving the first practical steps and getting oriented into this field is proving to be challenging to outsiders and beginners. In this paper, we review the most relevant tools and software for BNs structural and parameter learning to date, providing our subjective recommendations directed to an audience of beginners. In addition, we provide an extensive easy-to-consult overview table summarizing all software packages and their main features. By improving the reader understanding of which available software might best suit their needs, we improve accessibility to the field and make it easier for beginners to take their first step into it. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17025 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Guide to Bayesian Networks Software Packages for Structure and Parameter Learning -- 2025 Edition Gaudillo, Joverlyn Astrologo, Nicole Stella, Fabio Acerbi, Enzo Canonaco, Francesco Artificial Intelligence I.2 A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool for this task. BNs require constructing a structure of dependencies among variables and learning the parameters that govern these relationships. These tasks, referred to as structural learning and parameter learning, are actively investigated by the research community, with several algorithms proposed and no single method having established itself as standard. A wide range of software, tools, and packages have been developed for BNs analysis and made available to academic researchers and industry practitioners. As a consequence of having no one-size-fits-all solution, moving the first practical steps and getting oriented into this field is proving to be challenging to outsiders and beginners. In this paper, we review the most relevant tools and software for BNs structural and parameter learning to date, providing our subjective recommendations directed to an audience of beginners. In addition, we provide an extensive easy-to-consult overview table summarizing all software packages and their main features. By improving the reader understanding of which available software might best suit their needs, we improve accessibility to the field and make it easier for beginners to take their first step into it. |
| title | A Guide to Bayesian Networks Software Packages for Structure and Parameter Learning -- 2025 Edition |
| topic | Artificial Intelligence I.2 |
| url | https://arxiv.org/abs/2503.17025 |