Fast Algorithm for Non-Stationary Gaussian Process Prediction
Authors: Yulai Zhang, Guiming Luo
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Some experiments are veriļ¬ed on the real world power load data. |
| Researcher Affiliation | Academia | Yulai Zhang and Guiming Luo School of Software, Tsinghua University Beijing, 100084, China P.R. {zhangyl08@mails, gluo@mail}.tsinghua.edu.cn |
| Pseudocode | Yes | Figure 1: FNSGP (Fast Non-Stationary Gaussian Process Inference ) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | Our experiment is done on real world ultra short time electric power load data. The paper mentions the dataset but gives no indication of its public availability or access details like a link or formal citation. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions 'n = 1000, d = 30' which are model parameters, but it does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. |