Negative-Binomial Randomized Gamma Dynamical Systems for Heterogeneous Overdispersed Count Time Sequences
Authors: Rui Huang, Sikun Yang, Heinz Koeppl
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed relational gamma process dynamical systems, and compare it with closely-related methods, using both synthetic and real-world count data. |
| Researcher Affiliation | Academia | 1School of Computing and Information Technology, Great Bay University, 523000 Dongguan, China 2Great Bay Institute for Advanced Study, Great Bay University 3Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems, Great Bay University 4Dongguan Key Laboratory for Data Science and Intelligent Medicine, Great Bay University 5Department of Electrical Engineering and Information Technology, Technische Universit at Darmstadt |
| Pseudocode | No | The paper describes the inference procedure and algorithms (e.g., Gibbs sampling) in text but does not include any explicit pseudocode or algorithm blocks within the main body. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | We conducted the experiments with the following real-world datasets: (1) Integrated Crisis Early Warning System (ICEWS) dataset... [King, 2001; Stewart, 2014]; (2) Last.fm...; (3) Earthquake Reports Database (EQDB)...; (4) COVID-19... |
| Dataset Splits | No | We treated the 80 percent of the data as the training set, and the remaining 20 percent as the test set. The paper does not explicitly mention a separate validation split. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For NBRGDS, FS-NBRGDS and GS-NBRGDS, we choose K = 100 when V ≥ 1000, while the dimensions of EQDB and COVID-19 datasets are smaller than 100, thus we choos K = 25. We set C = K for FS-NBRGDS and GS-NBRGDS. We ran 5000 iterations of the Gibbs sampler, which have started to converge after 1000 iterations. We discarded the first 3000 samples which were treated as burn-in time and collected a posterior sample every tenth sample thereafter. |