Indirect Causes in Dynamic Bayesian Networks Revisited
Authors: Alexander Motzek, Ralf Möller
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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. |