The Multivariate Generalised von Mises Distribution: Inference and Applications
Authors: Alexandre Navarro, Jes Frellsen, Richard Turner
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate approximate inference on the applications outlined in Section 4 we present experiments on synthetic and real datasets. The results shown in Table 1 indicate that the m Gv M provides a better overall fit than the 1D-GP and the 2D-GP in all experiments. |
| Researcher Affiliation | Academia | Alexandre K. W. Navarro Department of Engineering University of Cambridge Cambridge, UK akwn2@cam.ac.uk Jes Frellsen Department of Engineering University of Cambridge Cambridge, UK jf519@cam.ac.uk Richard E. Turner Department of Engineering University of Cambridge Cambridge, UK ret26@cam.ac.uk |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'A comprehensive description and the data sets used in the all experiments conducted are available at http://tinyurl.com/mgvm-release', but it does not explicitly mention that the source code for the methodology is provided at this link or elsewhere. |
| Open Datasets | Yes | Five data sets were used in this evaluation. ... a dataset consisting Uber ride requests in NYC in April 20142, the tide levels predictions from the UK Hydrographic Office in 20163 as function of the latitude and longitude of a given port, ... 2https://github.com/fivethirtyeight/uber-tlc-foil-response 3http://www.ukho.gov.uk/Easytide/easytide/Select Port.aspx |
| Dataset Splits | No | The paper states 'a subset of the data points was kept for validation' but does not provide specific details on the split percentages, sample counts, or the methodology used for creating these splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software components, libraries, or frameworks used for the implementation. |
| Experiment Setup | No | The paper states 'Further experimental details are also provided in the Supplementary Material' but does not include specific hyperparameters (e.g., learning rate, batch size, number of epochs) or detailed system-level training settings in the main text. |