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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions
Authors: Christiane Görgen, Manuele Leonelli
JMLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our methods are demonstrated to be robust and comparable to standard ones, which can break the conditional independence structure of the model, using an artificial example and a medical real-world application. |
| Researcher Affiliation | Academia | Christiane G orgen EMAIL Max Planck Institute for Mathematics in the Sciences Inselstraße 22, 04103 Leipzig, Germany Manuele Leonelli EMAIL School of Human Sciences and Technology IE University Madrid, Spain |
| Pseudocode | No | The paper describes methods and proofs using mathematical notation but does not include any explicitly labeled pseudocode or algorithm blocks with structured steps. |
| Open Source Code | Yes | An implementation of the methods developed in this paper in the open-source R software (R Core Team, 2019) is given in the package bnmonitor and available at https://github. com/manueleleonelli/bnmonitor. |
| Open Datasets | Yes | In this section we study a subset of the data set of Eisner et al. (2011) including metabolomic information of 77 individuals: 47 of them suffering of cachexia, whilst the remaining do not. |
| Dataset Splits | No | The paper mentions analyzing a dataset split into two populations (ill and not ill) and generating random data for other networks, but it does not provide specific percentages, sample counts, or methodologies for train/test/validation splits for model evaluation. |
| Hardware Specification | Yes | Computations were carried out on a Intel Core I7 of 8th generation. |
| Software Dependencies | No | The paper mentions using "open-source R software (R Core Team, 2019)" and the "bnlearn R package (Scutari, 2010)", but it does not specify exact version numbers for these software components or any other key libraries. |
| Experiment Setup | No | For the artificial example, the paper sets specific model parameters (e.g., β0i = 0, vi = 1, β12 = 2) to construct a covariance matrix. For the real-world application, it states that GBN models were |