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].
Exact Algorithms for MRE Inference
Authors: Xiaoyuan Zhu, Changhe Yuan
JAIR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluations show that the proposed BFBn B algorithms make exact MRE inference tractable in Bayesian networks that could not be solved previously. [...] 6. Experiments |
| Researcher Affiliation | Academia | Xiaoyuan Zhu EMAIL Changhe Yuan EMAIL Queens College, City University of New York 65-30 Kissena Blvd., Queens, NY 11367 |
| Pseudocode | Yes | Algorithm 1 Compiling Minimal Target Blanket Decomposition [...] Algorithm 2 Merging Target Blankets [...] Algorithm 3 Splitting Target Blankets [...] Algorithm 4 Compile Belief Ratio Tables [...] Algorithm 5 BFBn B Algorithm Based on Target Blanket Upper Bounds |
| Open Source Code | No | The paper does not contain any explicit statement about making the source code available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | The proposed algorithms are evaluated on six benchmark diagnostic Bayesian networks listed in Table 1, i.e., Alarm (Ala), Carpo (Car), Hepar (Hep), Insurance (Ins), Emdec6h (Emd), and CPCS179 (Cpc) (Beinlich, Suermondt, Chavez, & Cooper, 1989; Binder, Koller, Russell, & Kanazawa, 1997; Onisko, 2003; Pradhan, Provan, Middleton, & Henrion, 1994). |
| Dataset Splits | No | In the 12-target setting, we randomly generated five test settings of each network, each setting consisting of all leaf nodes as evidence, 12 of the remaining nodes as targets, and others as auxiliary nodes. Then for each setting, we randomly generated 20 configurations of evidence (test cases) by sampling from the prior distributions of the networks. In the difficult-target setting, we randomly generated five test settings of each network, each setting consisting of all leaf nodes as evidence, around 20 of the remaining nodes as targets, and others as auxiliary nodes. The number of targets is selected so that the test cases are too challenging for BFBF but are still solvable by MPBnd and SPBnd. Then for each setting, we randomly generated 20 configurations of evidence (test cases) by sampling from the prior distributions of the networks. This describes how test cases are generated and target/evidence variables are selected, but not traditional training/validation/test dataset splits. |
| Hardware Specification | Yes | The experiments were performed on a 2.67GHz Intel Xeon CPU E7 with 512G RAM running a 3.7.10 Linux kernel. |
| Software Dependencies | No | The paper mentions "running a 3.7.10 Linux kernel" but does not specify any programming languages, libraries, or solvers with version numbers that are directly relevant to the methodology's implementation dependencies. |
| Experiment Setup | Yes | In MPBnd and SPBnd, we set the maximum number of targets in a target blanket K to be 18. In SPBnd, we set the maximum number of enclosed-targets in a target blanket N to be 7. [...] In tabu search, we set the number of search steps since the last improvement L and the maximum number of search steps M according to different network settings. In the 12-target setting, we set L to be 20 and M to be {400, 800, 1600, 3200, 6400}. In the difficult-target setting, we set L to be 80 and M to be {12800, 25600, 51200}. |