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..
Handling Overlaps When Lifting Gaussian Bayesian Networks
Authors: Mattis Hartwig, Tanya Braun, Ralf Möller
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Complexity analyses and experimental results show that despite overlaps constructing a lifted joint and answering queries on the lifted joint outperform their grounded counterparts significantly. |
| Researcher Affiliation | Academia | Mattis Hartwig1 , Tanya Braun1 , Ralf M oller2 1Institute of Information Systems, University of L ubeck, L ubeck, Germany 2 German Research Centre for Artificial Intelligence, L ubeck, Germany |
| Pseudocode | No | The paper provides mathematical formulas and descriptions but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes setting up an experiment with generated data ("We set up an experiment with 4 PRVs and 5 logvars that are partially shared. We increase the domain sizes of 2 logvars in exponential steps (from 2 to 27) resulting in around 20,000 randvars in the grounded model for the largest domains.") but does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We set up an experiment with 4 PRVs and 5 logvars that are partially shared. We increase the domain sizes of 2 logvars in exponential steps (from 2 to 27) resulting in around 20,000 randvars in the grounded model for the largest domains. For conditional query answering, we introduce evidence for the majority of the randvars and use a query set of 4 randvars. |