Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Posterior Consistency for Bayesian Relevance Vector Machines
Authors: Xiao Fang, Malay Ghosh
JMLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 16,362 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,880 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The present paper revisits their problem, introduces a new class of global-local priors di๏ฌerent from theirs, and provides results on posterior consistency as well as on posterior contraction rates. Keywords: Global-local priors; Posterior Contraction; Reproducing kernel Hilbert space. |
| Researcher Affiliation | Academia | Xiao Fang EMAIL Malay Ghosh EMAIL Department of Statistics University of Florida Gainesville, FL 32611, USA |
| Pseudocode | No | The paper describes mathematical models, derivations, and theorems. It does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about making source code available, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper refers to types of data problems (e.g., 'microarray experiments, image analysis, and a variety of commonly encountered problems') and prior work that used 'near infrared (NIR) spectroscopy' data, but it does not specify or provide access information for any dataset used in the current research, which is primarily theoretical. |
| Dataset Splits | No | The paper is theoretical and focuses on posterior consistency and contraction rates for Bayesian Relevance Vector Machines. It does not describe any experiments that would require dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and presents mathematical derivations. It does not mention any specific software or libraries with version numbers that would be required for replication. |
| Experiment Setup | No | The paper is theoretical, focusing on mathematical properties of Bayesian Relevance Vector Machines, and does not describe any experimental setup or hyperparameters used for empirical evaluation. |