Notice: 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. Full accuracy metrics and methodology are described in [1].
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
| 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 |