Bayesian Structure Learning by Recursive Bootstrap
Authors: Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov, Guy Koren, Gal Novik
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate that the proposed algorithm scales well to hundreds of variables, and learns better MAP models and more reliable causal relationships between variables, than other state-of-the-art-methods. (Abstract); 4 Experiments We use common networks and datasets to analyze B-RAI in three aspects: (1) computational efficiency compared to classic bootstrap, (2) model averaging, and (3) model selection. Experiments were performed using the Bayes net toolbox (Murphy, 2001). |
| Researcher Affiliation | Industry | Raanan Y. Rohekar Intel AI Lab raanan.yehezkel@intel.com Yaniv Gurwicz Intel AI Lab yaniv.gurwicz@intel.com Shami Nisimov Intel AI Lab shami.nisimov@intel.com Guy Koren Intel AI Lab guy.koren@intel.com Gal Novik Intel AI Lab gal.novik@intel.com |
| Pseudocode | Yes | Algorithm 1: Construct a graph generative tree, T; Algorithm 2: Sample a CPDAG from T |
| Open Source Code | No | No explicit statement about providing open-source code for the described methodology (B-RAI) is found. The paper only mentions using an existing toolbox. |
| Open Datasets | Yes | We use common networks2 and datasets3 to analyze B-RAI... 2www.bnlearn.com/bnrepository/ 3www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html |
| Dataset Splits | No | The paper specifies training and test dataset sizes (e.g., '500 samples for training' and '5000 samples for calculating the posterior predictive probability') but does not mention a separate validation split or explicit methodology for defining one. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory, or specific computing environments) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'Bayes net toolbox (Murphy, 2001)' but does not specify a version number for this software or any other dependencies. |
| Experiment Setup | Yes | Conditional mutual information was used for CI testing, and BDeu with ESS = 1 for scoring. (Section 4); We apply B-RAI, with s = 3, for different sample sizes... (Section 4.1); We set γ = 1 and use the Bayesian score, BDeu (Heckerman et al., 1995). (Section 3.3.1) |