Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Finding an ϵ-Close Minimal Variation of Parameters in Bayesian Networks
Authors: Bahare Salmani, Joost-Pieter Katoen
IJCAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 20,590 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,319 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that ϵ-close tuning of large BN benchmarks with up to eight parameters is feasible.Our experiments on our prototypical implementation indicate that ϵ-bounded tuning of up to 8 parameters for large networks with 100 variables is feasible. |
| Researcher Affiliation | Academia | Bahare Salmani and Joost-Pieter Katoen RWTH Aachen University EMAIL |
| Pseudocode | Yes | Algorithm 1: Minimal change tuning" and "Algorithm 2: R+-minimal distance instantiation |
| Open Source Code | Yes | 1https://github.com/baharslmn/pbn-epsilon-tuning |
| Open Datasets | Yes | We parametrized benchmarks from bnlearn repository and defined different constraints. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | Yes | We conducted all our experiments on a 2.3 GHz Intel Core i5 processor with 16 GB RAM. |
| Software Dependencies | Yes | We empirically evaluated our approach using a prototypical realization on top of the probabilistic model checker Storm [Hensel et al., 2022] (version 1.7.0). |
| Experiment Setup | Yes | The hyperparameters of the algorithm are the coverage factor 0 < η < 1, the region expansion factor 0 < γ < 1, and the maximum number of iterations K N.We took γ=1/2 and K=6 for our experiments, see Sec. 5.4. |