A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables
Authors: Zhao Tang Luo, Huiyan Sang, Bani Mallick
JMLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we illustrate the performance of the model with simulation studies and a real data analysis of detecting the temperature-salinity relationship from water masses in the Atlantic Ocean. |
| Researcher Affiliation | Academia | Zhao Tang Luo EMAIL Huiyan Sang EMAIL Bani Mallick EMAIL Department of Statistics Texas A&M University College Station, TX 77840, USA |
| Pseudocode | Yes | The RJ-MCMC algorithm is summarized in Algorithm 1 and detailed in Appendix B. |
| Open Source Code | No | The code will be made publicly available upon publication. |
| Open Datasets | Yes | The data of temperature and salinity is downloaded from National Oceanographic Data Center (https://www.nodc.noaa.gov/OC5/woa13/). |
| Dataset Splits | No | The paper describes generating 1000 spatial locations for simulation studies and choosing a random sample of 5,130 spatial locations for real data analysis, but it does not specify any training/test/validation splits for these datasets. |
| Hardware Specification | Yes | All computations were performed on a Linux server with two 2.4GHz 14-core processors and 64GB of memory. |
| Software Dependencies | No | We implement the BSCC method in R using the deldir package for the Delaunay triangulation, the igraph package for graph operations, and the ramcmc package for the Cholesky update/downdate. The implementation of the SCC method is adapted from the R package glmnet. The DPM model is implemented in R using the nimble code provided in Ma et al. (2020). |
| Experiment Setup | Yes | We consider four candidates α = 0.0075, 0.0150, 0.1000, 0.3333, which give c = 0.05, 0.1, 0.5, 0.9, respectively. The other hyperparameters are set to be a0 = b0 = 1 and c0 = d0 = 10 6, and the standard deviation for the random walk proposal in the hyper step of our RJ-MCMC algorithm is chosen to be 0.9. For each simulated data set, we run d = 8 tempered chains in parallel with the lowest inverse temperature td = 0.35. We run each chain for 100, 000 iterations, discarding the first 50, 000. We set the thinning interval to be 20 iterations and the swap interval to be 100. |