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 [1].
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 |