State-Space Abstractions for Probabilistic Inference: A Systematic Review
Authors: Stefan Lüdtke, Max Schröder, Frank Krüger, Sebastian Bader, Thomas Kirste
JAIR 2018 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This survey provides the following contributions. We perform a systematic literature review to outline the state of the art in probabilistic inference methods exploiting symmetries. From an initial set of more than 4,000 papers, we identify 116 relevant papers. Furthermore, we provide new high-level categories that classify the approaches, based on common properties of the approaches. |
| Researcher Affiliation | Academia | Stefan Lüdtke EMAIL Institute of Computer Science University of Rostock, Germany Max Schröder EMAIL Frank Krüger EMAIL Institute of Communications Engineering University of Rostock, Germany Sebastian Bader EMAIL Thomas Kirste EMAIL Institute of Computer Science University of Rostock, Germany |
| Pseudocode | No | The paper describes various algorithms like Variable Elimination, Recursive Conditioning, and Belief Propagation in detail, but it presents their concepts and mathematical formulations in paragraph form and equations, rather than in structured pseudocode or algorithm blocks. For example, Section 2.1.2 explains these algorithms without formal pseudocode blocks. |
| Open Source Code | No | The paper is a systematic review and does not present novel experimental methodology for which open-source code would be provided. It discusses existing methods but makes no statement about releasing its own code. |
| Open Datasets | No | The paper is a systematic literature review and does not conduct experiments using its own datasets. It refers to datasets and examples discussed in the papers it reviews (e.g., 'Friends and Smokers, Singla & Domingos, 2008', 'Office, Fox et al., 2003'), but it does not provide concrete access information for a dataset used by the paper's own methodology. |
| Dataset Splits | No | The paper is a systematic literature review and does not conduct experiments with datasets, therefore it does not provide training/test/validation dataset splits for reproducibility. |
| Hardware Specification | No | The paper is a systematic literature review and does not report on computational experiments that would require specifying hardware used. |
| Software Dependencies | No | The paper is a systematic literature review and does not describe a novel computational implementation for which specific software dependencies with version numbers would be required for replication. |
| Experiment Setup | No | The paper is a systematic literature review and does not describe its own experimental setup, hyperparameters, or training configurations, as it does not present novel experimental results. |