BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain
Authors: Zhao Tang Luo, Huiyan Sang, Bani Mallick
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the model performance by simulation experiments and a real chlorophyll data set in Aral Sea. |
| Researcher Affiliation | Academia | Zhao Tang Luo Department of Statistics Texas A&M University ztluo@stat.tamu.edu Huiyan Sang Department of Statistics Texas A&M University huiyan@stat.tamu.edu Bani Mallick Department of Statistics Texas A&M University bmallick@stat.tamu.edu |
| Pseudocode | No | The paper describes algorithms and steps for Bayesian inference in prose, but it does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of BAST is available at https://github.com/ztluostat/BAST. |
| Open Datasets | Yes | We apply BAST to analyze average remote sensed chlorophyll data in the Aral data over 1998-2002, which are available in the R package gamair [42]. |
| Dataset Splits | Yes | We first compare the prediction performance of all the models via 10-fold cross-validation. |
| Hardware Specification | Yes | All experiments are conducted on a single CPU core (Intel Xeon E5-2630 v4 CPU @ 2.20GHz) with 10GB of memory. |
| Software Dependencies | No | The paper mentions using the "R package gamair [42]" for a dataset, but it does not provide specific version numbers for the primary software components used to implement and run BAST (e.g., Python, PyTorch, TensorFlow, specific libraries). |
| Experiment Setup | Yes | We use M = 20 weak learners and set λk = 4 and k = 10 to restrict the size of each partition. ... The probabilities for MCMC moves are set as rb = rd = rc = 0.3 and rh = 0.1, with adjustments for cases where km = 1 or k. We run the MCMC for both BAST and BART for 20, 000 iterations, discarding the first half and retaining samples every 5 iterations. |