Learning Bayesian Networks from Ordinal Data
Authors: Xiang Ge Luo, Giusi Moffa, Jack Kuipers
JMLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through simulation studies, we illustrate the superior performance of the OSEM algorithm compared to the alternatives and analyze various factors that may influence the learning accuracy. Finally, we demonstrate the practicality of our method with a real-world application on psychological survey data from 408 patients with co-morbid symptoms of obsessive-compulsive disorder and depression. |
| Researcher Affiliation | Academia | Xiang Ge Luo EMAIL D-BSSE, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland Giusi Moffa Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland Division of Psychiatry, University College London, London, UK Jack Kuipers EMAIL D-BSSE, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland |
| Pseudocode | Yes | Algorithm 1: Ordinal Structural EM (OSEM) |
| Open Source Code | Yes | . R code is available at https://github.com/xgluo/OSEM |
| Open Datasets | Yes | For score-based approaches, we also evaluate the predictive performance using five real ordinal data sets from Mc Nally et al. (2017) and the UCI machine learning repository (Dua and Graff, 2017) (Table 1). |
| Dataset Splits | Yes | For each data set and method, we train the network with 80% of the data points, and conditioned on the structure, we compute the log loss on the remaining 20% test cases. |
| Hardware Specification | No | The runtimes are highly dependent on the algorithmic implementation and the software packages chosen (see Appendix B.7). No specific hardware (e.g., GPU, CPU model) is mentioned for running experiments. |
| Software Dependencies | No | Table S1 lists several R packages (e.g., pcalg, bnlearn, Bi DAG, MXM, rpcart) used for implementation, but it does not specify version numbers for these packages or the R environment itself. |
| Experiment Setup | Yes | We choose the Monte Carlo sample size K to be 5 and the penalty coefficient λ to be 6, which corresponds to the highest sum of average TPR and (1 FPRp) on the ROC curves for N = 500, n = 20 or 30, E[Li] = 4 or 5, and ν = 1 from our simulations studies. |