Bayesian Dynamic Mode Decomposition
Authors: Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, Takehisa Yairi
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate the empirical performance of Bayesian DMD using synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Department of Aeronautics and Astronautics, The University of Tokyo The Institute of Scientific and Industrial Research, Osaka University RIKEN Center for Advanced Intelligence Project |
| Pseudocode | Yes | Algorithm 1 (DMD [Tu et al., 2014]). |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology described in this paper is found. |
| Open Datasets | Yes | We chose locomotion data of three subjects (Subjects #2, #16 and #35), for which both walk and run/jog motions were recorded.1 Downloaded from http://mocap.cs.cmu.edu/. |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits (e.g., percentages, sample counts, or cross-validation details) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud resources) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In our implementation, the hyperparameters α and β were set to 10-3. |