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..
Indirect Causes in Dynamic Bayesian Networks Revisited
Authors: Alexander Motzek, Ralf Möller
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | By introducing activator random variables, we propose template fragments for modeling dynamic Bayesian networks under a causal use of time, anticipating indirect influences on a solid mathematical basis, obeying the laws of Bayesian networks. |
| Researcher Affiliation | Academia | Alexander Motzek and Ralf Möller Institute of Information Systems Universität zu Lübeck, Germany |
| Pseudocode | No | The paper describes mathematical derivations and operations textually but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper uses conceptual examples with illustrative values rather than an actual dataset, and therefore provides no access information for any training data. |
| Dataset Splits | No | The paper does not describe any empirical experiments using datasets, thus no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper focuses on theoretical concepts and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper focuses on theoretical concepts and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical concepts and does not describe any experimental setup details such as hyperparameters or training configurations. |