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.
Scalable and Robust Bayesian Inference via the Median Posterior
Authors: Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David Dunson
ICML 2014 | Venue PDF | LLM Run Details | Input Tokens: 17,917 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,474 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Numerical Experiments, 4.1. Simulated data, 4.2. Real data: Pd G hormone levels vs day of ovulation |
| Researcher Affiliation | Academia | Departments of Mathematics1 and Statistical Science2, Duke University, Durham, NC 27708 Statistical and Applied Mathematical Sciences Institute3, 19 T.W. Alexander Dr, Research Triangle Park, NC 27709 |
| Pseudocode | Yes | Algorithm 1 Evaluating the geometric median of probability distributions via Weiszfeld s algorithm, Algorithm 2 Approximating the M-posterior distribution |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the methodology described is publicly available. |
| Open Datasets | Yes | North Carolina Early Pregnancy Study (NCEPS) measured urinary pregnanediol-3-glucuronide (Pd G) levels, a progesterone metabolite, in 166 women from the day of ovulation across 41 time points (Baird et al., 1999). |
| Dataset Splits | Yes | The data was divided into 10 subsets. On each stage, 9 of them were used to evaluate the M-posterior while the remaining was a test set; the process was repeated 10 times for different test subsets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The gausspr function in kernlab R package (Karatzoglou et al., 2004) is used for GP regression. (No version numbers provided for R or kernlab) |
| Experiment Setup | Yes | the noise variance (or nugget effect) is fixed at 0.01., we set m = 10 and generate 100 samples from every Π100,10( |Gj,i), j = 1, . . . , 10 to form the empirical measures Qj,i |