Bayesian Structure Learning by Recursive Bootstrap
Authors: Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov, Guy Koren, Gal Novik
NeurIPS 2018 | Venue 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 EMAIL Yaniv Gurwicz Intel AI Lab EMAIL Shami Nisimov Intel AI Lab EMAIL Guy Koren Intel AI Lab EMAIL Gal Novik Intel AI Lab EMAIL |
| 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) |