Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Authors: Dominik Linzner, Michael Schmidt, Heinz Koeppl
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our method on synthetic and two real-world data sets. For all experiments, we consider a fixed set of hyper-parameters. |
| Researcher Affiliation | Academia | Dominik Linzner1 Michael Schmidt1 Heinz Koeppl1,2 1Department of Electrical Engineering and Information Technology 2Department of Biology Technische Universität Darmstadt {dominik.linzner, michael.schmidt, heinz.koeppl}@bcs.tu-darmstadt.de |
| Pseudocode | Yes | Algorithm 1 Stationary points of Euler Lagrange equation |
| Open Source Code | Yes | An implementation of our method is available via Git1. 1https://git.rwth-aachen.de/bcs/ssl-ctbn |
| Open Datasets | Yes | We applied our method to the British Household Panel Survey (ESRC Research Centre on Micro-social Change, 2003). |
| Dataset Splits | No | The paper describes how data was used (e.g., 'a varying number of trajectories', 'picked 600 at random') but does not specify explicit train/validation/test dataset splits with percentages or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'standard Matlab implementation of the interior-point method' but does not specify a version number for Matlab or any other software dependencies. |
| Experiment Setup | Yes | For all experiments, we consider a fixed set of hyper-parameters. We set the Dirichlet concentration parameter ci = 0.9 for all i {1, . . . , N}. Further, we assume a prior for the generators, which is uninformative on the structure αi(x, x | u) = 5 and βi(x | u) = 10, for all x, x Xi, u Ui. |