A Review of Inference Algorithms for Hybrid Bayesian Networks
Authors: Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
JAIR 2018 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we provide an overview of the main trends and principled approaches for performing inference in hybrid Bayesian networks. The methods covered in the paper are organized and discussed according to their methodological basis. |
| Researcher Affiliation | Collaboration | Antonio Salmer on EMAIL Rafael Rum ı EMAIL Dept. of Mathematics, University of Almer ıa 04120 Almer ıa, Spain Helge Langseth EMAIL Dept. of Computer and Information Science Norwegian University of Science and Technology 7491 Trondheim, Norway Thomas D. Nielsen EMAIL Dept. of Computer Science, Aalborg University 9220 Aalborg, Denmark Anders L. Madsen EMAIL Hugin Expert A/S 9000 Aalborg, Denmark and Dept. of Computer Science, Aalborg University 9220 Aalborg, Denmark |
| Pseudocode | No | The paper describes algorithms such as variable elimination and join tree algorithms but does not present them in structured pseudocode or algorithm blocks. The description is given in paragraph text and mathematical equations. |
| Open Source Code | No | The paper is a review of inference algorithms and existing software. While Table 1 lists some open-source software that implement these algorithms, the authors do not state that they are releasing source code for the methodology or review presented in this paper. |
| Open Datasets | No | This paper is a review of inference algorithms and does not present experimental results based on specific datasets. Therefore, no information about publicly available or open datasets is provided. |
| Dataset Splits | No | This paper is a review of inference algorithms and does not present experimental results. Therefore, it does not provide details about training/test/validation dataset splits. |
| Hardware Specification | No | This paper is a review of inference algorithms and does not present experimental results from the authors' own work. Therefore, no specific hardware used for running experiments is mentioned. |
| Software Dependencies | No | This paper is a review of inference algorithms and does not present experimental results from the authors' own work that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is a review of inference algorithms and does not present experimental results from the authors' own work. Therefore, no experimental setup details like hyperparameters or system-level training settings are provided. |